This paper introduces mGPT, a multilingual variant of GPT-3, pretrained on 61 languages from 25 linguistically diverse language families using Wikipedia and the C4 Corpus. We detail the design and pretraining procedure. The models undergo an intrinsic and extrinsic evaluation: language modeling in all languages, downstream evaluation on cross-lingual NLU datasets and benchmarks in 33 languages, and world knowledge probing in 23 languages. The in-context learning abilities are on par with the contemporaneous language models while covering a larger number of languages, including underrepresented and low-resource languages of the Commonwealth of Independent States and the indigenous peoples in Russia. The source code and the language models are publicly available under the MIT license.

The advent of the Transformer architecture (Vaswani et al., 2017) has facilitated the development of various language models (LMs; Liu et al., 2020a). Although the well-established “pretrain & finetune” paradigm has led to rapid progress in NLP (Wang et al., 2019), it imposes several limitations. Finetuning relies on an extensive amount of labeled data. Collecting high-quality labeled data for new tasks and languages is expensive and resource-consuming (Wang et al., 2021). LMs can learn spurious correlations from finetuning data (Naik et al., 2018; Niven and Kao, 2019) and demonstrate inconsistent generalization, catastrophic forgetting, or brittleness to finetuning data order (McCoy et al., 2020; Dodge et al., 2020). Last but not least, finetuning requires additional computational resources and, therefore, aggravates the problem of a large carbon footprint (Bender et al., 2021).

The latest approaches address these limitations with zero-shot and few-shot learning, performing a task with LM scoring or conditioning on a few demonstration examples without parameter updates (Brown et al., 2020). Autoregressive LMs adopted via these paradigms have been widely applied in many NLP tasks (Schick and Schütze, 2021; Perez et al., 2021), notably in cross-lingual knowledge transfer (Winata et al., 2021) and low-resource language scenarios (Lin et al., 2022). However, model development for underrepresented typologically distant and low-resource languages (Wu and Dredze, 2020; Lauscher et al., 2020; Hedderich et al., 2021) and cross-lingual generalization abilities of autoregressive LMs (Erdem et al., 2022) have been left understudied.

This paper presents mGPT, a multilingual version of GPT-3 (Brown et al., 2020) available in 1.3B (mGPT1.3B) and 13B (mGPT13B) parameters. We aim (i) to develop a large-scale multilingual autoregressive LM that inherits the GPT-3’s generalization benefits and (ii) to increase the linguistic diversity of multilingual LMs, making the first attempt to address languages of the Commonwealth of Independent States (CIS) and under-resourced languages of the indigenous peoples in Russia. We pretrain mGPT in 61 languages from 25 language families on Wikipedia and Colossal Clean Crawled Corpus (C4; Raffel et al., 2020). We analyze the mGPT’s performance on various intrinsic and extrinsic tasks and compare it with the contemporaneous generative LMs.

Key Findings

The analysis reveals that (i) mGPT13B is comparable to XGLM1.7B (Lin et al., 2022) while having fewer weights and covering a larger number of languages, (ii) mGPT shows confident performance on Austronesian, Austro-Asiatic, Japonic, Germanic, and Romance languages on multiple tasks and prominent language modeling abilities on the languages of the indigenous peoples in Russia, (iii) adding more demonstrations may result in performance degradation for both mGPT and XGLM, and (iv) hate speech detection is one of the most challenging tasks, receiving random guessing performance in the zero-shot and few-shot evaluation setups. External validation by the NLP community since the release1 shows that mGPT1.3B can outperform large-scale LMs on SuperGLUE tasks and promote strong solutions for multilingual clause-level morphology tasks. We release the model evaluation code,2 the mGPT1.3B3 and mGPT13B4 models. We hope to facilitate research on the applicability of autoregressive LMs in non-English languages and increase the linguistic inclusivity of the low-resource languages.

Multilingual Transformers

Recent years have featured the development of various monolingual and multilingual LMs initially designed for English. BERT (Devlin et al., 2019) has been replicated in other high-resource languages (Martin et al., 2020; Masala et al., 2020) and language families, e.g., Indian (Kakwani et al., 2020) and Balto-Slavic (Arkhipov et al., 2019). Massively multilingual LMs—mBERT, XLM-R (Conneau et al., 2020), RemBERT (Chung et al., 2021), mBART (Liu et al., 2020b) and mT5 (Xue et al., 2021)—have now pushed state-of-the-art results on various NLP tasks in multiple languages (Kalyan et al., 2021). Such models support more than 100 languages and vary in the architecture design and pretraining objectives. By contrast, our work presents one of the first multilingual autoregressive LMs covering more than 61 languages.

GPT-based Language Models

Large-scale generative LMs (e.g., GPT-3; Brown et al., 2020) are triggering a shift from the “pretrain & finetune” paradigm to prompt-based learning (Liu et al., 2023a). The benefit of balancing the pretraining costs and performing standardized NLP tasks with a few demonstration examples has stimulated the development of open-source autoregressive LMs for English (e.g., Black et al., 2022; Biderman et al., 2023; Dey et al., 2023), Chinese (Zeng et al., 2021), and Russian (Zmitrovich et al., 2023). A few contemporaneous works extend the research on zero-shot and few-shot learning, evaluating the in-context abilities of GPT-based LMs in multilingual scenarios. Winata et al. (2021) report that English GPTs perform significantly better than random guessing with monolingual and multilingual prompts on typologically close languages, such as French, Spanish, and German. Lin et al. (2022) propose XGLM, a multilingual GPT-style LM in 30 languages, and empirically show that it can outperform its monolingual counterparts of the comparable number of parameters. We use XGLM as the main baseline in our experiments and analyze the results of comparing mGPT1.3B with other autoregressive LMs published after our release, such as BLOOM (Scao et al., 2023).

3.1 Pretraining Data

Language Selection

Table 1 summarizes the list of languages by their family. The pretraining corpus consists of a typologically weighted set of languages covered by cross-lingual benchmarks, such as XGLUE (Liang et al., 2020) and XTREME (Hu et al., 2020). The motivation behind the language choices is to narrow the gap between the high-resource and low-resource languages (Ducel et al., 2022). To this end, we include 20 languages from the tail of the C4 language list, the list of underrepresented languages of Russia, and the official and resource-lean CIS languages Orekhov et al., 2016.

Table 1: 

A list of languages by the language family.

Language FamilyLanguages
Afro-Asiatic Arabic (ar), Hebrew (he) 
Austro-Asiatic Vietnamese (vi) 
Austronesian Indonesian (id), Javanese (jv), Malay (ms) 
Tagalog (tl) 
Baltic Latvian (lv), Lithuanian (lt) 
Basque Basque (eu) 
Dravidian Malayalam (ml), Tamil (ta), Telugu (te) 
Indo-European (Armenian) Armenian (hy) 
Indo-European (Indo-Aryan) Bengali (bn), Marathi (mr), Hindi (hi), 
Urdu (ur) 
Indo-European (Germanic) Afrikaans (af), Danish (da), English (en), 
German (de), Swedish (sv) 
Indo-European (Romance) French (fr), Italian (it), Portuguese (pt), 
Romanian (ro), Spanish (es) 
Indo-European (Greek) Greek (el) 
Indo-European (Iranian) Ossetian (os), Tajik (tg), Persian (fa) 
Japonic Japanese (ja) 
Kartvelian Georgian (ka) 
Koreanic Korean (ko) 
Kra-Dai Thai (th) 
Mongolic Buryat (bxr), Kalmyk (xal), Mongolian (mn) 
Niger-Congo Swahili (sw), Yoruba (yo) 
Slavic Belarusian (be), Bulgarian (bg), Russian (ru), 
Ukrainian (uk), Polish (pl) 
Sino-Tibetan Burmese (my) 
Turkic (Karluk) Uzbek (uz) 
Turkic (Kipchak) Bashkir (ba), Kazakh (kk), Kyrgyz (ky), 
Tatar (tt) 
Turkic (Oghuz) Azerbaijani (az), Chuvash (cv), Turkish (tr), 
 Turkmen (tk) 
Turkic (Siberian) Tuvan (tyv), Yakut (sax) 
Uralic Estonian (et), Finnish (fi), Hungarian (hu) 
Language FamilyLanguages
Afro-Asiatic Arabic (ar), Hebrew (he) 
Austro-Asiatic Vietnamese (vi) 
Austronesian Indonesian (id), Javanese (jv), Malay (ms) 
Tagalog (tl) 
Baltic Latvian (lv), Lithuanian (lt) 
Basque Basque (eu) 
Dravidian Malayalam (ml), Tamil (ta), Telugu (te) 
Indo-European (Armenian) Armenian (hy) 
Indo-European (Indo-Aryan) Bengali (bn), Marathi (mr), Hindi (hi), 
Urdu (ur) 
Indo-European (Germanic) Afrikaans (af), Danish (da), English (en), 
German (de), Swedish (sv) 
Indo-European (Romance) French (fr), Italian (it), Portuguese (pt), 
Romanian (ro), Spanish (es) 
Indo-European (Greek) Greek (el) 
Indo-European (Iranian) Ossetian (os), Tajik (tg), Persian (fa) 
Japonic Japanese (ja) 
Kartvelian Georgian (ka) 
Koreanic Korean (ko) 
Kra-Dai Thai (th) 
Mongolic Buryat (bxr), Kalmyk (xal), Mongolian (mn) 
Niger-Congo Swahili (sw), Yoruba (yo) 
Slavic Belarusian (be), Bulgarian (bg), Russian (ru), 
Ukrainian (uk), Polish (pl) 
Sino-Tibetan Burmese (my) 
Turkic (Karluk) Uzbek (uz) 
Turkic (Kipchak) Bashkir (ba), Kazakh (kk), Kyrgyz (ky), 
Tatar (tt) 
Turkic (Oghuz) Azerbaijani (az), Chuvash (cv), Turkish (tr), 
 Turkmen (tk) 
Turkic (Siberian) Tuvan (tyv), Yakut (sax) 
Uralic Estonian (et), Finnish (fi), Hungarian (hu) 

Data Preparation Pipeline

Pretraining extensive LMs requires large volumes of high-quality data. Despite the explosive growth of web corpora resulting in the pretraining data volume of up to 6T tokens (Xue et al., 2021), the data quality is often unsatisfactory (Kreutzer et al., 2022). General approaches to maximizing the quality are based on manually curated heuristics (Yang et al., 2019b), the perplexity of LMs (Wenzek et al., 2020), and data quality classifiers (Brown et al., 2020). Our data preparation pipeline includes data collection, deduplication, and filtration.

Data Collection

The pretraining corpus represents a collection of documents from Wikipedia and C4. The Wikipedia texts are extracted from the dumps (v. 20201101) with WikiExtractor (Attardi, 2015). The C4 data is downloaded using the Tensorflow datasets5 (Paper, 2021).

Deduplication

The text deduplication includes 64-bit hashing of each text in the pretraining corpus for keeping texts with a unique hash.

Filtration

We follow Ortiz Suárez et al. (2019) on the C4 data filtration. We also filter the documents based on their text compression rate using zlib.6 The most strongly and weakly compressing deduplicated texts are discarded. The compression range for an acceptable text is empirically defined as ×1.2 to ×8. The texts with an entropy of less than 1.2 contain code junk and entities, while those of more than 8 contain repetitive segments. The next step includes distinguishing between low and high-quality documents with a binary classifier. The classifier is trained with Vowpal Wabbit7 on the Wikipedia documents as positive examples and the filtered C4 documents as negative ones. The remainder is cleaned by a set of language-agnostic heuristics. The size of the pretraining corpus is 46B (Wikipedia), and 442B UTF characters (C4), resulting in 600GB. Figure 1 shows the total number of tokens for each language, and the total number of documents in the pretraining corpus is presented in Figure 2.

Figure 1: 

Number of tokens for each language in the pretraining corpus on a logarithmic scale.

Figure 1: 

Number of tokens for each language in the pretraining corpus on a logarithmic scale.

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Figure 2: 

Number of documents for each language in the pretraining corpus on a logarithmic scale.

Figure 2: 

Number of documents for each language in the pretraining corpus on a logarithmic scale.

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3.2 Tokenization

The design of the tokenization method may have a significant impact on learning efficient representations, model memorization, and downstream performance (Mielke et al., 2021; Nogueira et al., 2021; Pfeiffer et al., 2021; Rust et al., 2021). We investigate the effect of the tokenization strategy on the model perplexity. We pretrain five strategy-specific versions of mGPT163M on a Wikipedia subset of the pretraining corpus. The tokenization strategy is selected based on their perplexity on a held-out Wikipedia sample (approx. 10.7MB), which is inferred as Equation 1.
PPL(t)=exp(1ci=0tlogpθ(xix<i))
(1)
where t is an input text, |t| is the length of the text in tokens, |c| is the length of the text in characters. The perplexity is normalized over the number of characters since the tokenizers produce different numbers of tokens for t (Cotterell et al., 2018).

Tokenization Strategies

We considered five tokenization strategies incorporating specific representations of uppercase characters, numbers, punctuation marks, and whitespaces. Table 2 presents examples of the tokenization strategies.

  • default: BBPE (Wang et al., 2020);

  • case: Each uppercase character is replaced with a special token <case> followed by the corresponding lowercase character;

  • arithmetic: The case strategy combined with representing numbers and arithmetic operations as individual tokens;

  • combined: The arithmetic strategy combined with representing punctuation marks and whitespaces as individual tokens;

  • char: Character-level tokenization.

Table 2: 

Different tokenization strategies applied to the sentence “22 Birds + 3 birds = 25 birds”. The resulting tokens are highlighted in the corresponding colors.

Different tokenization strategies applied to the sentence “22 Birds + 3 birds = 25 birds”. The resulting tokens are highlighted in the corresponding colors.
Different tokenization strategies applied to the sentence “22 Birds + 3 birds = 25 birds”. The resulting tokens are highlighted in the corresponding colors.

Pretraining Details

The models are pretrained on 16 V100 GPUs for 600k training steps with a set of fixed hyperparameters: vocabulary size of 100k, context window of 2048, learning rate of 2e−4, and batch size of 4.

Results

The experiment results are presented in Table 3. The default model achieves the best results, outperforming the rest of the models by up to 2.5 of perplexity score. Based on this experiment, we select the default strategy to pretrain the mGPT1.3B and mGPT13B models.

Table 3: 

The average perplexity results. The best score is put in bold, the second best is underlined.

StrategyAvg. PPL
default 6.94 
case 8.13 
arithmetic 7.99 
combined 8.43 
char 9.47 
StrategyAvg. PPL
default 6.94 
case 8.13 
arithmetic 7.99 
combined 8.43 
char 9.47 

3.3 Model Architecture

The mGPT architecture is based on GPT-3. We use the architecture description by Brown et al., the GPT-2 code base (Radford et al., 2019) from HuggingFace (Wolf et al., 2020), and Megatron- LM (Shoeybi et al., 2020). Table 4 presents the description of the GPT-2 and GPT-3 architectures of comparable sizes. With all the other hyperparameters equal, GPT-3 has fewer layers (Layers: 48 vs. 24) but a larger hidden size (dmodel: 1600 vs. 2048) as opposed to GPT-2. GPT-3 also alternates the classic dense and sparse attention layers (Child et al., 2019).

Table 4: 

Comparison of GPT-2 and GPT-3. The mGPT architecture replicates the parameters of GPT-31.3B and GPT-313B, and uses sparse attention in alternating dense and sparse layers.

ModelSizeLayersdmodel
GPT-2 1.5B 48 1600 
GPT-31.3B 1.3B 24 2048 
GPT-313B 13B 40 5120 
ModelSizeLayersdmodel
GPT-2 1.5B 48 1600 
GPT-31.3B 1.3B 24 2048 
GPT-313B 13B 40 5120 

3.4 Model Pretraining

The pretraining procedure mostly follows Brown et al. We utilize the DeepSpeed library (Rasley et al., 2020) and Megatron-LM (Shoeybi et al., 2020). We pretrain our LMs with a total batch size of 2048 and a context window of 512 tokens. The total number of the training steps is 600k, and the models have seen 400B tokens during pretraining. The pretraining took 14 days on a cluster of 256 V100 GPUs for mGPT1.3B and 22 days on 512 V100 GPUs for mGPT13B. We report the computational, energy, and carbon costs in §7.2.

4.1 Language Modeling

Method

We estimate the language modeling performance on the held-out sets for each language. Here, perplexity is computed as described in §3.2, except that perplexity is normalized over the length of the input text t in tokens |t|. We also run statistical tests to analyze the effect of linguistic, dataset, and model configuration criteria:

  • Language script: We divide the languages into two groups by their scrip—Latin and others (e.g., Cyrillic and Arabic)—and use the Mann-Whitney U test (Mann and Whitney, 1947) to analyze the perplexity distributions in the groups.

  • Pretraining corpus size: We calculate the Pearson correlation coefficient (Pearson, 1895) to analyze the correlation between the language perplexity and the number of documents in this language in the pretraining corpus.

  • Model size: We use the Mann-Whitney U test to analyze the effect of the model size.

Results by Language

Figure 3 presents the perplexity scores for each language on the held-out sets. The mGPT13B model achieves the best perplexities within the 2-to-10 score range for the majority of languages, including Dravidian (Malayalam, Tamil, Telugu), Indo-Aryan (Bengali, Hindi, Marathi), Slavic (Belarusian, Ukrainian, Russian, Bulgarian), Sino-Tibetan (Burmese), Kipchak (Bashkir, Kazakh), and others. Higher perplexities up to 20 are for only seven languages from different families. The mGPT1.3B results have similar distribution but are consistently higher than mGPT13B.

Figure 3: 

Language-wise perplexity results. Lower is better.

Figure 3: 

Language-wise perplexity results. Lower is better.

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Results by Language Family

Analyzing results by the language family (see Figure 4), we find that mGPT13B shows consistently lower perplexities as opposed to mGPT1.3B. Specifically, mGPT1.3B underperforms mGPT13B on Basque, Greek, Kartvelian, and Turkic families.

Figure 4: 

Family-wise perplexity results. The scores are averaged over the number of languages within each family.

Figure 4: 

Family-wise perplexity results. The scores are averaged over the number of languages within each family.

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Correlation Analysis

We present the results in Table 5. We observe that the language modeling performance depends on the language script and model size. In particular, the non-Latin languages receive lower scores on average, while mGPT13B performs better than mGPT1.3B in this setting. However, the positive correlation between the pretraining corpus size and perplexity in particular languages can be attributed to the low diversity of the text domains in the pretraining monolingual corpora for the low-resource languages. Such corpora contain Wikipedia articles on a limited amount of general topics; therefore, the model learns the distribution in the corpora without being able to generalize well. In general, the results align with Scao et al. (2023), who report that the considered criteria can affect the knowledge acquired by BLOOM1B and BLOOM176B.

Table 5: 

Correlation analysis results.

CriterionModelTestp-value
Language script mGPT1.3B M-W U test 0.012 
mGPT13B 0.000 
Pretraining corpus size mGPT1.3B Pearson 0.137 
mGPT13B 0.307 
Model size mGPT1.3B M-W U test 0.0007 
mGPT13B 
CriterionModelTestp-value
Language script mGPT1.3B M-W U test 0.012 
mGPT13B 0.000 
Pretraining corpus size mGPT1.3B Pearson 0.137 
mGPT13B 0.307 
Model size mGPT1.3B M-W U test 0.0007 
mGPT13B 

4.2 Downstream Evaluation

We conduct an extrinsic evaluation of mGPT and baselines on classification and sequence labeling tasks in zero-shot and few-shot settings. In the zero-shot setting, the model is shown a test example formatted as a prompt in natural language, while in the few-shot setting, the model is provided with k demonstrations from the training data specified via prompts. The prompt examples for each task are presented in Table 6.

Table 6: 

Prompt examples for each downstream task. The examples are in English for illustration purposes.

TaskTemplateOutput Candidates
XNLI <s> {sentence 1}, right? {label} {sentence 2} </s> Yes (Entailment); Also (Neutral)
No (Contradiction) 
PAWSX <s> {sentence 1}, right? {label} {sentence 2} </s> Yes; No 
XWINO <s> {sentence start}{candidate} {sentence end} </s> ✗ 
XCOPA <s> {sentence} because {candidate answer} </s> ✗ 
<s> {sentence} so {candidate answer} </s> 
Hate Speech <s> The sentence is {label}. {sentence} </s> sexist, racist, offensive, abusive, hateful (Positive) 
normal, common, ok, usual, acceptable (Negative) 
NER <s>lang: {lang}∖n Tagged sentence: {sentence with tags} I-LOC, I-MISC, 
I-ORG, I-PER, O 
POS <s>lang: {lang}∖n Tagged sentence: {sentence with tags} ADJ, ADP, ADV, AUX, 
CCONJ, DET, INTJ, NOUN, 
NUM, PART, PRON, PROPN, PUNCT, 
SCONJ, SYM, VERB, X 
TaskTemplateOutput Candidates
XNLI <s> {sentence 1}, right? {label} {sentence 2} </s> Yes (Entailment); Also (Neutral)
No (Contradiction) 
PAWSX <s> {sentence 1}, right? {label} {sentence 2} </s> Yes; No 
XWINO <s> {sentence start}{candidate} {sentence end} </s> ✗ 
XCOPA <s> {sentence} because {candidate answer} </s> ✗ 
<s> {sentence} so {candidate answer} </s> 
Hate Speech <s> The sentence is {label}. {sentence} </s> sexist, racist, offensive, abusive, hateful (Positive) 
normal, common, ok, usual, acceptable (Negative) 
NER <s>lang: {lang}∖n Tagged sentence: {sentence with tags} I-LOC, I-MISC, 
I-ORG, I-PER, O 
POS <s>lang: {lang}∖n Tagged sentence: {sentence with tags} ADJ, ADP, ADV, AUX, 
CCONJ, DET, INTJ, NOUN, 
NUM, PART, PRON, PROPN, PUNCT, 
SCONJ, SYM, VERB, X 

4.2.1 Classification

Tasks

The classification tasks include commonsense reasoning (XCOPA; Ponti et al., 2020), natural language inference (XNLI; Conneau et al.2018), Winograd schema challenge (XWINO; Tikhonov and Ryabinin, 2021), paraphrase detection (PAWSX; Yang et al., 2019a), and hate speech detection (Davidson et al., 2017).

Method

mGPT utilizes per-token cross-entropy loss, which is reduced to negative log probability due to one-hot encoding of the tokens. We select the target label associated with the prompt that results in the lowest sum of negative log probabilities for its tokens. The few-shot experiments are run five times with different random seeds, while the zero-shot experiments are run only once since the model loss is determined.

Baselines

The XGLM1.7B and XGLM7.5B models are used as the baselines in the classification experiments. We reproduce the XGLM evaluation based on the methodology by Lin et al. (2022) and use the model weights and code available in the fairseq8 library (Ott et al., 2019). We select prompts according to the templates reported by Lin et al. Prompts for non-English languages are automatically translated with Google Translate.

Results

Table 7 presents the classification results averaged across languages. The “✗” tag marks k-shot settings not reported by Lin et al. We do not perform them for reproducibility purposes and fair comparison. The results by Lin et al. are reproduced in the zero-shot setup, and some scores are even slightly higher. However, not all results are reproduced, e.g., PAWSX and XNLI. We attribute this to potential differences in the translated prompts.

Table 7: 

Accuracy scores (%) on classification tasks averaged across languages.

Modelk-shotXWINOPAWSXXCOPAXNLIHate Speech
mGPT1.3B 56.2 53.1 55.5 40.6 50.0 
57.0 51.3 54.9 36.1 ✗ 
56.8 52.2 54.8 37.4 50.8 
16 54.5 52.2 54.8 37.9 ✗ 
mGPT13B 59.3 51.5 58.2 42.6 53.1 
61.0 50.6 57.9 37.5 ✗ 
61.8 51.6 58.3 41.4 51.5 
16 59.2 55.1 57.3 33.3 ✗ 
XGLM1.7B 54.2 50.3 55.5 42.6 50.1 
58.0 45.9 56.8 36.4 ✗ 
57.9 45.9 56.2 38.8 49.5 
16 ✗ 44.2 56.1 36.5 ✗ 
XGLM7.5B 59.2 50.1 55.5 44.7 50.1 
63.7 46.4 60.6 36.9 ✗ 
64.2 45.3 61.4 40.1 51.8 
16 ✗ 44.9 62.5 40.0 ✗ 
Modelk-shotXWINOPAWSXXCOPAXNLIHate Speech
mGPT1.3B 56.2 53.1 55.5 40.6 50.0 
57.0 51.3 54.9 36.1 ✗ 
56.8 52.2 54.8 37.4 50.8 
16 54.5 52.2 54.8 37.9 ✗ 
mGPT13B 59.3 51.5 58.2 42.6 53.1 
61.0 50.6 57.9 37.5 ✗ 
61.8 51.6 58.3 41.4 51.5 
16 59.2 55.1 57.3 33.3 ✗ 
XGLM1.7B 54.2 50.3 55.5 42.6 50.1 
58.0 45.9 56.8 36.4 ✗ 
57.9 45.9 56.2 38.8 49.5 
16 ✗ 44.2 56.1 36.5 ✗ 
XGLM7.5B 59.2 50.1 55.5 44.7 50.1 
63.7 46.4 60.6 36.9 ✗ 
64.2 45.3 61.4 40.1 51.8 
16 ✗ 44.9 62.5 40.0 ✗ 

Overall, we observe that mGPT1.3B is comparable with XGLM1.7B while having fewer weights and is pretrained in twice as many languages. mGPT13B performs better than XGLM7.5B in zero-shot setting on all tasks except XNLI. At the same time, it lags behind in a few-shot setting being better than XGLM7.5B only in XNLI and PAWSX tasks. Comparing the performance across languages, we find that English receives the highest accuracy for all tasks. The mGPT1.3B and mGPT13B models show high accuracy for the Austronesian, Dravidian, Japonic, Germanic, and Romance language families. Only the Afro-Asiatic family gets low accuracy. The mGPT models perform better than the XGLM counterparts for Austronesian, Koreanic, and Romance languages.

Our results on hate speech detection are consistent with Lin et al. The performance is slightly better across the five languages but still close to random guessing (see Table 8). The manual analysis shows that the behavior is sensitive to the input prompts, most notably for Polish. Increasing the number of demonstrations can lead to performance degradation on some classification tasks for both mGPT and XGLM.

Table 8: 

Accuracy scores (%) on hate speech detection by language. The best score is put in bold, the second best is underlined.

Modelk-shotenesptplit
mGPT1.3B 55.1 52.1 42.3 50.0 50.2 
50.1 50.2 51.7 51.5 50.4 
mGPT13B 59.0 55.2 46.9 50.0 54.6 
52.2 50.0 50.8 53.4 51.0 
XGLM1.7B 54.8 51.8 52.3 50.0 54.5 
51.0 48.8 49.2 46.7 51.0 
XGLM7.5B 61.7 52.4 52.3 50.0 49.0 
51.8 51.3 51.5 51.4 52.9 
Modelk-shotenesptplit
mGPT1.3B 55.1 52.1 42.3 50.0 50.2 
50.1 50.2 51.7 51.5 50.4 
mGPT13B 59.0 55.2 46.9 50.0 54.6 
52.2 50.0 50.8 53.4 51.0 
XGLM1.7B 54.8 51.8 52.3 50.0 54.5 
51.0 48.8 49.2 46.7 51.0 
XGLM7.5B 61.7 52.4 52.3 50.0 49.0 
51.8 51.3 51.5 51.4 52.9 

4.2.2 Sequence Labeling

Tasks

The sequence labeling tasks include named entity recognition (NER) and part-of-speech tagging (POS) from the XGLUE benchmark (Liang et al., 2020). To address other medium-resource and resource-lean languages, we use the Universal Dependencies treebanks (UD; Nivre et al., 2016) to evaluate POS-tagging in Armenian, Belarusian, Buryat, Kazakh, Tatar, Ukrainian, and Yakut.

Method

We use a modified approach to the sequence labeling tasks compared to §4.2.1. Given a sentence of n words, we iteratively predict the label for each word xi using the preceding words x <i and their predicted labels l <i as the context using a template “x<il<i_”, where i is the current token index and “_” is a placeholder. The only exception is the first token xi used as the context. The placeholder is filled with each possible target label lL at each step. We select the label with the lowest sum of losses per token in the resulting string. The experiments are run in the zero-shot and 4-shot settings.9

Example

Consider an example for the POS-tagging task \I [PRON] want [VERB] it [PART] . [PUNCT]”, which requires 4 procedure steps. First, we combine the placeholder in the string \I_” with each possible POS tag and select the most probable candidate. Next, we repeat the procedure for \I_li want_”, and so on.

Baselines

We use results reported in Liang et al. as the baselines: M-BERT, XLM-R, and Unicoder (Huang et al., 2019). Note that the baselines are finetuned on the corresponding training set. The performance is evaluated with the F1-score (NER) and the accuracy score (POS-tagging)10 according to the XGLUE methodology.

NER Results

Table 9 shows counterintuitively that mGPT1.3B outperforms mGPT13B on all languages. 4-shot falls behind finetuned models but significantly outperforms random guessing for both mGPT models. Per-language language analysis shows a large gap between English and other languages (for mGPT13B the F1-score on English is more than twice higher than for any of the other languages), while for German, both models perform the worst. This pattern coincides with the baseline results. In addition, it could be noted that while for mGPT1.3B the F1-score exceeds the 10 percent threshold for all languages, this is not the case for mGPT13B.

Table 9: 

F1-scores for NER by language. The mGPT models are evaluated in the 4-shot setting. The best score is put in bold, the second best is underlined.

ModeldeenesnlAvg.
Random 1.9 3.1 1.8 1.6 2.1 
 
mGPT1.3B 12.2 22.1 12.7 13.1 15.0 
mGPT13B 5.6 20.9 10.4 6.7 10.9 
 
M-BERTbase 69.2 90.6 75.4 77.9 78.2 
XLM-Rbase 70.4 90.9 75.2 79.5 79.0 
Unicoder 71.8 91.1 74.4 81.6 79.7 
ModeldeenesnlAvg.
Random 1.9 3.1 1.8 1.6 2.1 
 
mGPT1.3B 12.2 22.1 12.7 13.1 15.0 
mGPT13B 5.6 20.9 10.4 6.7 10.9 
 
M-BERTbase 69.2 90.6 75.4 77.9 78.2 
XLM-Rbase 70.4 90.9 75.2 79.5 79.0 
Unicoder 71.8 91.1 74.4 81.6 79.7 

POS-tagging Results

POS-tagging results for the XGLUE benchmark and resource-lean languages are presented in Table 10. Similarly to the NER task, mGPT1.3B outperforms mGPT13B practically in all languages except for Italian. On average mGPT1.3B achieves accuracy score of 0.24 while mGPT13B only scores 0.21. These results are still far behind fine-tuned models; however, they are significantly higher than random guessing. Analyzing the results for the low-resource languages, it can be seen that mGPT1.3B performance is comparable with its performance on XGLUE, while the mGPT13B scores are lower.

Table 10: 

Accuracy scores (%) for XGLUE and Universal Dependencies POS-tagging by language. mGPT models are evaluated in the 4-shot setting. The best score is put in bold, the second best is underlined.

ModelXGLUECIS & Low-Resource UD
arbgdeelenesfrhiitnlplptruthtrurvizhAvg.bebxrhykksahttuk
Random 6.5 6.5 6.0 5.2 4.4 5.7 5.5 6.7 6.6 6.6 5.9 4.7 6.0 6.4 6.8 1.2 7.0 7.1 5.8 1.3 5.7 5.9 2.6 9.6 8.7 4.8 
 
mGPT1.3B 16.5 24.5 30.6 20.9 40.0 24.3 27.0 16.2 25.4 28.8 28.3 24.6 29.4 12.9 30.4 15.0 25.6 19.5 24.4 21.5 28.4 14.7 22.8 19.9 21.4 22.5 
mGPT13B 11.7 21.8 26.8 16.1 36.0 22.2 25.0 12.3 26.5 26.5 24.2 21.8 21.8 9.5 26.8 12.7 21.5 12.5 20.9 10.6 7.7 7.3 9.4 11.8 9.2 10.9 
 
M-BERTbase 52.4 85.0 88.7 81.5 95.6 86.8 87.6 58.4 91.3 88.0 81.8 88.3 78.8 43.3 69.2 53.8 54.3 58.3 74.7 ✗ ✗ ✗ ✗ ✗ ✗ ✗ 
XLM-Rbase 67.3 88.8 92.2 88.2 96.2 89.0 89.9 74.5 92.6 88.5 85.4 89.7 86.9 57.9 72.7 62.1 55.2 60.4 79.8 ✗ ✗ ✗ ✗ ✗ ✗ ✗ 
Unicoder 68.6 88.5 92.0 88.3 96.1 89.1 89.4 69.9 92.5 88.9 83.6 89.8 86.7 57.6 75.0 59.8 56.3 60.2 79.6 ✗ ✗ ✗ ✗ ✗ ✗ ✗ 
ModelXGLUECIS & Low-Resource UD
arbgdeelenesfrhiitnlplptruthtrurvizhAvg.bebxrhykksahttuk
Random 6.5 6.5 6.0 5.2 4.4 5.7 5.5 6.7 6.6 6.6 5.9 4.7 6.0 6.4 6.8 1.2 7.0 7.1 5.8 1.3 5.7 5.9 2.6 9.6 8.7 4.8 
 
mGPT1.3B 16.5 24.5 30.6 20.9 40.0 24.3 27.0 16.2 25.4 28.8 28.3 24.6 29.4 12.9 30.4 15.0 25.6 19.5 24.4 21.5 28.4 14.7 22.8 19.9 21.4 22.5 
mGPT13B 11.7 21.8 26.8 16.1 36.0 22.2 25.0 12.3 26.5 26.5 24.2 21.8 21.8 9.5 26.8 12.7 21.5 12.5 20.9 10.6 7.7 7.3 9.4 11.8 9.2 10.9 
 
M-BERTbase 52.4 85.0 88.7 81.5 95.6 86.8 87.6 58.4 91.3 88.0 81.8 88.3 78.8 43.3 69.2 53.8 54.3 58.3 74.7 ✗ ✗ ✗ ✗ ✗ ✗ ✗ 
XLM-Rbase 67.3 88.8 92.2 88.2 96.2 89.0 89.9 74.5 92.6 88.5 85.4 89.7 86.9 57.9 72.7 62.1 55.2 60.4 79.8 ✗ ✗ ✗ ✗ ✗ ✗ ✗ 
Unicoder 68.6 88.5 92.0 88.3 96.1 89.1 89.4 69.9 92.5 88.9 83.6 89.8 86.7 57.6 75.0 59.8 56.3 60.2 79.6 ✗ ✗ ✗ ✗ ✗ ✗ ✗ 

4.3 Knowledge Probing

Method

We probe our models for factual knowledge in 23 languages using the mLAMA dataset (Kassner et al., 2021). The task is to complete a knowledge triplet <subject, relation, object> converted to templates for querying LMs. Consider an example from the original LAMA (Petroni et al., 2019) for English, where <Dante, born-in, X> is converted to the template “Dante was born in [MASK]”. We follow Lin et al. to design the probing task. As each such query contains hundreds of negative candidates on average, we limit the number of candidates to three, i.e., one is the ground truth candidate and the other two candidates are randomly sampled from the provided knowledge source. The probing performance is evaluated with precision@1 averaged over all relations per language.

Results

Figure 5 outlines the results for mGPT1.3B and mGPT13B. The overall pattern is that the performance is equal to or above 0.6 for Germanic, Romance, Austro-Asiatic, Japonic, and Chinese languages. However, Uralic, Slavic, Koreanic, and Afro-Asiatic languages receive scores of lower than 0.5. We also find that scaling the number of model parameters usually boosts the performance for high-resource languages up to 5 points, while no significant improvements are observed in the other languages. Comparing our results with Lin et al., we conclude that our models achieve lower performance than XGLM7.5B almost in all languages and perform on par with GPT3-Curie6.5B.

Figure 5: 

Knowledge probing results for 23 languages. The performance of a random baseline is 0.33.

Figure 5: 

Knowledge probing results for 23 languages. The performance of a random baseline is 0.33.

Close modal

4.4 External Evaluation

General Language Understanding

Scao et al. (2023) compared the performance of BLOOM176B, mGPT1.3B, OPT175B (Zhang et al., 2022), GPT-J6B (Wang and Komatsuzaki, 2021), and T011B (Victor et al., 2022) on subset of tasks from the SuperGLUE benchmark (Wang et al., 2019) in the zero-shot and one-shot settings. The results of evaluating the models using five prompts are presented in Figure 6. The mGPT1.3B model has comparable performance despite having fewer weights. In the zero-shot setting, the performance of mGPT1.3B, BLOOM176B, OPT175B, and GPT-J6B on the considered tasks is above random guessing. We also observe the strong performance of mGPT1.3B on the Winogender Schema Diagnostics (Ax-g). In the one-shot setting, mGPT1.3B performs on par with GPT-J6B, and the resulting variability is significantly reduced across all prompts.

Figure 6: 

The SuperGLUE evaluation results in the zero-shot and one-shot settings (Scao et al., 2023).

Figure 6: 

The SuperGLUE evaluation results in the zero-shot and one-shot settings (Scao et al., 2023).

Close modal

Multilingual Clause-level Morphology

The first shared task on Multilingual Clause-level Morphology (Goldman et al., 2022) covers nine languages and includes three sub-tasks: (i) inflection (generating a word form given a lexeme and a set of morphosyntactic features), (ii) reinflection (reinflect an input sentence according to a given set of morphosyntactic features), and (iii) detect a root and its features in an input sentence. Acikgoz et al. (2022) develop a first-place solution based on mGPT1.3B and prefix-tuning method, outperforming other solutions and baselines on the third task.

4.5 Generation Evaluation

Method

We compute seven lexical diversity metrics from Gehrmann et al. (2021) using the mGPT outputs11 on 100 test set samples from the story generation task in five languages: English, French, German, Spanish, and Chinese (Chen et al., 2022). The diversity metrics include the Shannon Entropy over unigrams (Entropy1), the mean segmented type-token ratio over segment lengths of 100 (MSTTR), the ratio of distinct unigrams over the total number of unigrams (Distinct1), and the counter of unigrams that appear once in the collection of generated outputs (Unique1).

Results

The results are presented in Table 11. The diversity metrics scores for Chinese are the highest, while the mean generated text length is the shortest. This is likely due to its logographic writing. The results for the Indo-European languages are similar (French, German, and Spanish), indicating that mGPT1.3B generates diverse texts in these languages. Surprisingly, the metrics are lower for English, with the average text length being longer. Our current natural language generation evaluation approach lacks downstream tasks, which we leave for future work.

Table 11: 

The results for lexical diversity of generated texts on the GEM story generation task.

ISOAvg. lengthDistinct1Vocabulary sizeUnique1Entropy1TTRMSTTR
en 39.13 ± 22.61 0.071 387 103 6.175 0.097 0.228 
fr 23.53 ± 17.92 0.128 486 181 6.875 0.159 0.346 
de 30.85 ± 17.33 0.113 453 159 6.850 0.151 0.340 
es 12.71 ± 15.54 0.102 413 124 6.818 0.148 0.315 
zh 3.157 ± 2.39 0.492 188 124 7.055 0.525 0.526 
ISOAvg. lengthDistinct1Vocabulary sizeUnique1Entropy1TTRMSTTR
en 39.13 ± 22.61 0.071 387 103 6.175 0.097 0.228 
fr 23.53 ± 17.92 0.128 486 181 6.875 0.159 0.346 
de 30.85 ± 17.33 0.113 453 159 6.850 0.151 0.340 
es 12.71 ± 15.54 0.102 413 124 6.818 0.148 0.315 
zh 3.157 ± 2.39 0.492 188 124 7.055 0.525 0.526 

Our key takeaways on pretraining and evaluating large-scale multilingual autoregressive LMs are summarized below.

5.1 Model Scaling

Empirical Results

The language modeling results for mGPT1.3B and mGPT13B suggest that the model scaling improves its generation abilities for all given languages (see §4.1). However, it does not improve performance on the downstream and probing tasks (see §4.2; §4.3). Overall, the language modeling performance depends on the model size and the pretraining corpus size in a language, and smaller models may better encode linguistic information than larger ones. These findings align with Scao et al. (2023).

Takeaways

Our work had been conducted a year before the Chinchilla scaling laws were introduced (Hoffmann et al., 2022). According to the advanced methods of scaling LMs, our pretraining corpus can be sufficiently extended to improve the generalization abilities of the mGPT13B model. At the same time, the pretraining corpus design can promote the model underfitting and overfitting on particular languages. We believe it can be accounted for by aggregating the language-specific cross-entropy loss and producing language weights similar to Xie et al. (2023).

5.2 Lack of Data

Empirical Results

Another challenging factor is the lack of high-quality data for the low-resource languages. Although mGPT shows promising results on the language modeling and sequence labeling tasks for the underrepresented languages (see §4.1, §4.2), the low amount of evaluation resources limits the scope of analyzing the model generalization abilities. The correlation between the model performance and the amount of pretraining data in a language (see §4.1, and, e.g., Lauscher et al., 2020; Ahuja et al., 2022) further highlights the need for creating text corpora in such languages.

Takeaways

The question of addressing the discrepancy in data distribution across the world’s languages remains unresolved. Our data collection and filtration approach is equivalent for all considered languages. Extending the language-agnostic heuristics is restrained due to the lack of linguistic expertise. However, we assume that experimenting with the training data for the text quality classifiers can improve the resulting quality of the corpora for the low-resource languages (e.g., training the classifiers on different mixtures of data in the medium and high-resource languages).

As the follow-up work, we release 23 versions of the mGPT1.3B model continuously pretrained with language modeling objective on monolingual corpora for medium-resource and low-resource languages collected through collaboration with the NLP community. Table 12 summarizes the models by language and the language modeling performance on the held-out monolingual test sets. Examples of the corpora include Eastern Armenian National Corpus (Khurshudyan et al., 2022), OpenSubtitles (Lison and Tiedemann, 2016), and TED talks. Continued pretraining on additional data improves the language modeling performance.

Table 12: 

A list of the mGPT1.3B models continuously pretrained on monolingual corpora for 23 languages.

5.3 Language Selection

Empirical Results

Results of mGPT1.3B on most of the classification tasks are on par or better than the results of the XGLM1.7B given that mGPT covers twice as many languages (see §4.2). However, mGPT underperforms the baselines on several multi-class classification and probing tasks.

Takeaways

We find that balancing the pretraining corpus by the language family helps improve the language modeling abilities for underrepresented languages due to their typological similarity with the medium and high-resource languages (see §4.1). However, increasing language diversity can lead to performance degradation because of the curse of multilinguality and a limited model capacity (Conneau et al., 2020).

5.4 Tokenization

Empirical Results

We conduct an ablation study to analyze the impact of the tokenization strategy on language modeling performance. We find that the considered strategies do not improve the model’s perplexity. However, the main drawback of the perplexity-based evaluation is that it only partially assesses the model generalization abilities.

Takeaways

The optimal tokenization method and vocabulary size remain an open question, particularly in the multilingual setup (Mielke et al., 2021). There are no established methods for defining the vocabulary size based on the amount of textual data in different languages. Our experiments are limited to a fixed vocabulary size, and we leave further investigation of the tokenization strategies and their configurations for future work.

5.5 Zero-shot and Few-shot Performance

Empirical Results

  • Increasing the number of demonstrations does not always lead to improvements but decreases the performance on some downstream tasks (see §4.2.1; §4.2.2). This observation aligns with Lin et al. (2022) and Brown et al. (2020).

  • The zero-shot and few-shot performance may not exceed the random guessing on particular tasks, which points to the failure of a model to follow the guidance in the demonstration examples (see §4.2.1; §4.2.2).

  • The prompting approach is unstable and hardly universal across languages, as indicated by the model sensitivity to the prompts.

  • The mGPT models can assign higher probabilities to the most frequent tag in the input for the sequence labeling tasks (see §4.2.2).

Takeaways

  • The stability of the models with respect to the prompts may be improved using prompt-tuning (Liu et al., 2023b) and contextual calibration (Zhao et al., 2021) as shown in §4.4.

  • The generalization capabilities of the autoregressive LMs in sequence labeling tasks is an underexplored area. While our LMs achieve results higher than random guessing, the low performance can be attributed to the probability distribution shifts between the pretraining corpora and the prompts. We leave the investigation of the alternative prompt design (Liu et al., 2023a) and structured prediction methods (Liu et al., 2022) for future work.

We introduce the mGPT1.3B and mGPT13B models, which cover 61 languages from linguistically diverse 25 language families. Our model is one of the first autoregressive LMs for economically endangered and underrepresented CIS and low-resource languages. The architecture design choices are based on the preliminary tokenization experiments and their perplexity-based evaluation. The model evaluation experiments include language modeling, standardized cross-lingual NLU datasets and benchmarks, world knowledge probing, and social bias tasks. We evaluate the in-context learning abilities in zero and few-shot settings with a negative log-likelihood probability. We present a detailed analysis of the model performance, limitations, and ethical considerations. Despite the space for further quality growth and solving the highlighted limitations, the model shows significant potential and can become the basis for developing generative pipelines for languages other than English, especially the low-resource ones. This initiative has been developed for 23 diverse languages through collaboration with the NLP community. We hope to benefit cross-lingual knowledge transfer, annotation projection, and other potential applications for economically challenged and underrepresented languages and diversify the research field by shifting from the Anglo-centric paradigm.

7.1 Low-resource Languages

NLP for resource-lean scenarios is one of the leading research directions nowadays. The topic’s relevance has led to proactive research on low-resource languages. Our work falls under this scope, introducing the first autoregressive LM for 61 languages. To the best of our knowledge, we present one of the first attempts to address this problem for 20 languages of the Commonwealth of Independent States and the indigenous peoples in Russia.

7.2 Energy Efficiency and Usage

Pretraining large-scale LMs requires many computational resources, which is energy-intensive and expensive. To address this issue, we used the sparse attention approach suggested by Brown et al. (2020) and reduced the computational resources required to achieve the desired performance. The CO2 emission of pretraining the mGPT models is computed as Equation 2 (Strubell et al., 2019):
CO2=PUE*kWh*ICO21000
(2)

The power usage effectiveness (PUE) of our data centers is not more than 1.3, the spent power is 30.6k kWh (mGPT1.3B) and 91.3 kWh (mGPT13B), and the CO2 energy intensity (ICO2) in the region is 400 grams per kWh. The resulting CO2 emission is 15.9k kg (mGPT1.3B) and 47.5k kg (mGPT13B). The emission is comparable with a single medium-range flight of a modern aircraft, which usually releases about 12k kg of CO2 per 1k km. Despite the costs, mGPT can be efficiently adapted to the user needs via few-shot learning, bringing down potential budget costs in the scope of applications in multiple languages, such as generating the content, augmenting labeled data, or summarizing news. The multilingual pretraining saves on data annotation and energy consumption, alleviating the carbon footprint. Model compression techniques, e.g., pruning and distillation, can reduce inference costs.

7.3 Social Risks of Harm

Stereotypes and unjust discrimination present in pretraining corpora lead to representation biases in LMs. LMs can reflect historical prejudices against disadvantaged social groups and reproduce harmful stereotypes about gender, race, religion, or sexual orientation (Weidinger et al., 2022). We have analyzed mGPT’s limitations on social risks of harm involving hate speech on the hate speech detection task. Our results are similar to Lin et al. (2022) in that the performance is close to random guessing. This may indicate a significant bias in the pretraining corpus, a mutual influence of languages during training, or methodological problems in the test set. We do not claim that our evaluation setup is exhaustive, and we assume that other biases can be revealed through a direct model application or an extended evaluation.

7.4 Potential Misuse

The misuse potential of LMs increases with their ability to generate high-quality texts. Malicious users can perform a socially harmful activity that involves generating texts, e.g., spreading propaganda and other targeted manipulation (Jawahar et al., 2020). We recognize that our models can be misused in all supported languages. However, adversarial defense and artificial text detection models can mitigate ethical and social risks of harm. Our primary purpose is to propose multilingual GPT-style LMs for research and development needs, and we hope to work on the misuse problem with other developers and experts in mitigation research in the future.

1 

As of the time of writing this paper, mGPT1.3B was publicly available. Note that mGPT13B is also now released.

9 

We report the results only in the 4-shot setting since the manual analysis reveals that the models have failed to capture the task, giving constant predictions without any additional examples.

10 

We evaluate the sequence labeling tasks using the XGLUE code: github.com/microsoft/XGLUE.

11 

We use the generation hyperparameters: temperature = 1, max_length = 100, top_k = 5, top_p = 0.9.

Emre Can
Acikgoz
,
Tilek
Chubakov
,
Muge
Kural
,
Gözde
Şahin
, and
Deniz
Yuret
.
2022
.
Transformers on multilingual clause-level morphology
. In
Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)
, pages
100
105
,
Abu Dhabi, United Arab Emirates (Hybrid)
.
Association for Computational Linguistics
.
Kabir
Ahuja
,
Shanu
Kumar
,
Sandipan
Dandapat
, and
Monojit
Choudhury
.
2022
.
Multi task learning for zero shot performance prediction of multilingual models
. In
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
, pages
5454
5467
,
Dublin, Ireland
.
Association for Computational Linguistics
.
Mikhail
Arkhipov
,
Maria
Trofimova
,
Yuri
Kuratov
, and
Alexey
Sorokin
.
2019
.
Tuning multilingual transformers for language-specific named entity recognition
. In
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
, pages
89
93
,
Florence, Italy
.
Association for Computational Linguistics
.
Giusepppe
Attardi
.
2015
.
WikiExtractor
. https://github.com/attardi/wikiextractor
Emily M.
Bender
,
Timnit
Gebru
,
Angelina
McMillan-Major
, and
Shmargaret
Shmitchell
.
2021
.
On the dangers of stochastic parrots: Can language models be too big?
In
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
, pages
610
623
.
Stella
Biderman
,
Hailey
Schoelkopf
,
Quentin Gregory
Anthony
,
Herbie
Bradley
,
Kyle
O’Brien
,
Eric
Hallahan
,
Mohammad Aflah
Khan
,
Shivanshu
Purohit
,
USVSN
Sai Prashanth
,
Edward
Raff
, et al.
2023
.
Pythia: A suite for analyzing large language models across training and scaling
. In
International Conference on Machine Learning
, pages
2397
2430
.
PMLR
.
Sidney
Black
,
Stella
Biderman
,
Eric
Hallahan
,
Quentin
Anthony
,
Leo
Gao
,
Laurence
Golding
,
Horace
He
,
Connor
Leahy
,
Kyle
McDonell
,
Jason
Phang
,
Michael
Pieler
,
Usvsn Sai
Prashanth
,
Shivanshu
Purohit
,
Laria
Reynolds
,
Jonathan
Tow
,
Ben
Wang
, and
Samuel
Weinbach
.
2022
.
GPT-NeoX-20B: An open- source autoregressive language model
. In
Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models
, pages
95
136
,
virtual+Dublin
.
Association for Computational Linguistics
.
Tom
Brown
,
Benjamin
Mann
,
Nick
Ryder
,
Melanie
Subbiah
,
Jared D.
Kaplan
,
Prafulla
Dhariwal
,
Arvind
Neelakantan
,
Pranav
Shyam
,
Girish
Sastry
,
Amanda
Askell
,
Sandhini
Agarwal
,
Ariel
Herbert-Voss
,
Gretchen
Krueger
,
Tom
Henighan
,
Rewon
Child
,
Aditya
Ramesh
,
Daniel
Ziegler
,
Jeffrey
Wu
,
Clemens
Winter
,
Chris
Hesse
,
Mark
Chen
,
Eric
Sigler
,
Mateusz
Litwin
,
Scott
Gray
,
Benjamin
Chess
,
Jack
Clark
,
Christopher
Berner
,
Sam
McCandlish
,
Alec
Radford
,
Ilya
Sutskever
, and
Dario
Amodei
.
2020
.
Language models are few-shot learners
. In
Advances in Neural Information Processing Systems
,
volume 33
, pages
1877
1901
.
Curran Associates, Inc.
Yiran
Chen
,
Zhenqiao
Song
,
Xianze
Wu
,
Danqing
Wang
,
Jingjing
Xu
,
Jiaze
Chen
,
Hao
Zhou
, and
Lei
Li
.
2022
.
MTG: A benchmark suite for multilingual text generation
. In
Findings of the Association for Computational Linguistics: NAACL 2022
, pages
2508
2527
,
Seattle, United States
.
Association for Computational Linguistics
.
Rewon
Child
,
Scott
Gray
,
Alec
Radford
, and
Ilya
Sutskever
.
2019
.
Generating long sequences with sparse transformers
.
Hyung Won
Chung
,
Thibault
Fevry
,
Henry
Tsai
,
Melvin
Johnson
, and
Sebastian
Ruder
.
2021
.
Rethinking embedding coupling in pre-trained language models
. In
International Conference on Learning Representations
.
Alexis
Conneau
,
Kartikay
Khandelwal
,
Naman
Goyal
,
Vishrav
Chaudhary
,
Guillaume
Wenzek
,
Francisco
Guzmán
,
Edouard
Grave
,
Myle
Ott
,
Luke
Zettlemoyer
, and
Veselin
Stoyanov
.
2020
.
Unsupervised cross-lingual representation learning at scale
. In
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
, pages
8440
8451
,
Online
.
Association for Computational Linguistics
.
Alexis
Conneau
,
Ruty
Rinott
,
Guillaume
Lample
,
Adina
Williams
,
Samuel
Bowman
,
Holger
Schwenk
, and
Veselin
Stoyanov
.
2018
.
XNLI: Evaluating cross-lingual sentence representations
. In
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
, pages
2475
2485
,
Brussels, Belgium
.
Association for Computational Linguistics
.
Ryan
Cotterell
,
Sabrina J.
Mielke
,
Jason
Eisner
, and
Brian
Roark
.
2018
.
Are all languages equally hard to language-model?
In
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
, pages
536
541
,
New Orleans, Louisiana
.
Association for Computational Linguistics
.
Thomas
Davidson
,
Dana
Warmsley
,
Michael
Macy
, and
Ingmar
Weber
.
2017
.
Automated hate speech detection and the problem of offensive language
. In
Proceedings of the International AAAI Conference on Web and Social Media
, volume
11
, pages
512
515
.
Jacob
Devlin
,
Ming-Wei
Chang
,
Kenton
Lee
, and
Kristina
Toutanova
.
2019
.
BERT: Pre-training of deep bidirectional transformers for language understanding
. In
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
, pages
4171
4186
,
Minneapolis, Minnesota
.
Association for Computational Linguistics
.
Nolan
Dey
,
Gurpreet
Gosal
,
Zhiming
,
Chen
,
Hemant
Khachane
,
William
Marshall
,
Ribhu
Pathria
,
Marvin
Tom
, and
Joel
Hestness
.
2023
.
Cerebras-GPT: Open compute-optimal language models trained on the cerebras wafer-scale cluster
.
Jesse
Dodge
,
Gabriel
Ilharco
,
Roy
Schwartz
,
Ali
Farhadi
,
Hannaneh
Hajishirzi
, and
Noah
Smith
.
2020
.
Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
.
Fanny
Ducel
,
Karën
Fort
,
Gaël
Lejeune
, and
Yves
Lepage
.
2022
.
Do we name the languages we study? The #BenderRule in LREC and ACL articles
. In
Proceedings of the Thirteenth Language Resources and Evaluation Conference
, pages
564
573
,
Marseille, France
.
European Language Resources Association
.
Erkut
Erdem
,
Menekse
Kuyu
,
Semih
Yagcioglu
,
Anette
Frank
,
Letitia
Parcalabescu
,
Barbara
Plank
,
Andrii
Babii
,
Oleksii
Turuta
,
Aykut
Erdem
,
Iacer
Calixto
, et al.
2022
.
Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning
.
Journal of Artificial Intelligence Research
,
73
:
1131
1207
.
Sebastian
Gehrmann
,
Tosin
Adewumi
,
Karmanya
Aggarwal
,
Pawan Sasanka
Ammanamanchi
,
Anuoluwapo
Aremu
,
Antoine
Bosselut
,
Khyathi Raghavi
Chandu
,
Miruna-Adriana
Clinciu
,
Dipanjan
Das
,
Kaustubh
Dhole
,
Wanyu
Du
,
Esin
Durmus
,
Ondřej
Dušek
,
Chris Chinenye
Emezue
,
Varun
Gangal
,
Cristina
Garbacea
,
Tatsunori
Hashimoto
,
Yufang
Hou
,
Yacine
Jernite
,
Harsh
Jhamtani
,
Yangfeng
Ji
,
Shailza
Jolly
,
Mihir
Kale
,
Dhruv
Kumar
,
Faisal
Ladhak
,
Aman
Madaan
,
Mounica
Maddela
,
Khyati
Mahajan
,
Saad
Mahamood
,
Bodhisattwa Prasad
Majumder
,
Pedro Henrique
Martins
,
Angelina
McMillan-Major
,
Simon
Mille
,
Emiel
van Miltenburg
,
Moin
Nadeem
,
Shashi
Narayan
,
Vitaly
Nikolaev
,
Andre Niyongabo
Rubungo
,
Salomey
Osei
,
Ankur
Parikh
,
Laura
Perez-Beltrachini
,
Niranjan Ramesh
Rao
,
Vikas
Raunak
,
Juan Diego
Rodriguez
,
Sashank
Santhanam
,
João
Sedoc
,
Thibault
Sellam
,
Samira
Shaikh
,
Anastasia
Shimorina
,
Marco Antonio
Sobrevilla Cabezudo
,
Hendrik
Strobelt
,
Nishant
Subramani
,
Wei
Xu
,
Diyi
Yang
,
Akhila
Yerukola
, and
Jiawei
Zhou
.
2021
.
The GEM benchmark: Natural language generation, its evaluation and metrics
. In
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
, pages
96
120
,
Online
.
Association for Computational Linguistics
.
Omer
Goldman
,
Francesco
Tinner
,
Hila
Gonen
,
Benjamin
Muller
,
Victoria
Basmov
,
Shadrack
Kirimi
,
Lydia
Nishimwe
,
Benoît
Sagot
,
Djamé
Seddah
,
Reut
Tsarfaty
, and
Duygu
Ataman
.
2022
.
The MRL 2022 shared task on multilingual clause-level morphology
. In
Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)
, pages
134
146
,
Abu Dhabi, United Arab Emirates (Hybrid)
.
Association for Computational Linguistics
.
Michael A.
Hedderich
,
Lukas
Lange
,
Heike
Adel
,
Jannik
Strötgen
, and
Dietrich
Klakow
.
2021
.
A survey on recent approaches for natural language processing in low-resource scenarios
. In
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
, pages
2545
2568
,
Online
.
Association for Computational Linguistics
.
Jordan
Hoffmann
,
Sebastian
Borgeaud
,
Arthur
Mensch
,
Elena
Buchatskaya
,
Trevor
Cai
,
Eliza
Rutherford
,
Diego de Las
Casas
,
Lisa Anne
Hendricks
,
Johannes
Welbl
,
Aidan
Clark
,
Tom
Hennigan
,
Eric
Noland
,
Katie
Millican
,
George
van den Driessche
,
Bogdan
Damoc
,
Aurelia
Guy
,
Simon
Osindero
,
Karen
Simonyan
,
Erich
Elsen
,
Jack W.
Rae
,
Oriol
Vinyals
, and
Laurent
Sifre
.
2022
.
Training compute-optimal large language models
.
Junjie
Hu
,
Sebastian
Ruder
,
Aditya
Siddhant
,
Graham
Neubig
,
Orhan
Firat
, and
Melvin
Johnson
.
2020
.
XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
. In
International Conference on Machine Learning
, pages
4411
4421
.
PMLR
.
Haoyang
Huang
,
Yaobo
Liang
,
Nan
Duan
,
Ming
Gong
,
Linjun
Shou
,
Daxin
Jiang
, and
Ming
Zhou
.
2019
.
Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks
. In
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
, pages
2485
2494
,
Hong Kong, China
.
Association for Computational Linguistics
.
Ganesh
Jawahar
,
Muhammad
Abdul-Mageed
, and
Laks Lakshmanan
,
V. S.
2020
.
Automatic detection of machine generated text: A critical survey
. In
Proceedings of the 28th International Conference on Computational Linguistics
, pages
2296
2309
,
Barcelona, Spain (Online)
.
International Committee on Computational Linguistics
.
Divyanshu
Kakwani
,
Anoop
Kunchukuttan
,
Satish
Golla
,
Gokul
N. C.
,
Avik
Bhattacharyya
,
Mitesh M.
Khapra
, and
Pratyush
Kumar
.
2020
.
IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages
. In
Findings of the Association for Computational Linguistics: EMNLP 2020
, pages
4948
4961
,
Online
.
Association for Computational Linguistics
.
Katikapalli Subramanyam
Kalyan
,
Ajit
Rajasekharan
, and
Sivanesan
Sangeetha
.
2021
.
AMMUS: A survey of transformer-based pretrained models in natural language processing
.
Nora
Kassner
,
Philipp
Dufter
, and
Hinrich
Schütze
.
2021
.
Multilingual LAMA: Investigating knowledge in multilingual pretrained language models
. In
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
, pages
3250
3258
,
Online
.
Association for Computational Linguistics
.
Victoria
Khurshudyan
,
Timofey
Arkhangelskiy
,
Misha
Daniel
,
Vladimir
Plungian
,
Dmitri
Levonian
,
Alex
Polyakov
, and
Sergei
Rubakov
.
2022
.
Eastern Armenian national corpus: State of the art and perspectives
. In
Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference
, pages
28
37
,
Marseille, France
.
European Language Resources Association
.
Julia
Kreutzer
,
Isaac
Caswell
,
Lisa
Wang
,
Ahsan
Wahab
,
Daan
van Esch
,
Nasanbayar
Ulzii-Orshikh
,
Allahsera
Tapo
,
Nishant
Subramani
,
Artem
Sokolov
,
Claytone
Sikasote
,
Monang
Setyawan
,
Supheakmungkol
Sarin
,
Sokhar
Samb
,
Benoît
Sagot
,
Clara
Rivera
,
Annette
Rios
,
Isabel
Papadimitriou
,
Salomey
Osei
,
Pedro Ortiz
Suarez
,
Iroro
Orife
,
Kelechi
Ogueji
,
Andre Niyongabo
Rubungo
,
Toan Q.
Nguyen
,
Mathias
Müller
,
André
Müller
,
Shamsuddeen Hassan
Muhammad
,
Nanda
Muhammad
,
Ayanda
Mnyakeni
,
Jamshidbek
Mirzakhalov
,
Tapiwanashe
Matangira
,
Colin
Leong
,
Nze
Lawson
,
Sneha
Kudugunta
,
Yacine
Jernite
,
Mathias
Jenny
,
Orhan
Firat
,
Bonaventure F. P.
Dossou
,
Sakhile
Dlamini
,
Nisansa
de Silva
,
Sakine Çabuk
Ballı
,
Stella
Biderman
,
Alessia
Battisti
,
Ahmed
Baruwa
,
Ankur
Bapna
,
Pallavi
Baljekar
,
Israel Abebe
Azime
,
Ayodele
Awokoya
,
Duygu
Ataman
,
Orevaoghene
Ahia
,
Oghenefego
Ahia
,
Sweta
Agrawal
, and
Mofetoluwa
Adeyemi
.
2022
.
Quality at a glance: An audit of web-crawled multilingual datasets
.
Transactions of the Association for Computational Linguistics
,
10
:
50
72
.
Anne
Lauscher
,
Vinit
Ravishankar
,
Ivan
Vulić
, and
Goran
Glavaš
.
2020
.
From zero to hero: On the limitations of zero-shot language transfer with multilingual Transformers
. In
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
, pages
4483
4499
,
Online
.
Association for Computational Linguistics
.
Yaobo
Liang
,
Nan
Duan
,
Yeyun
Gong
,
Ning
Wu
,
Fenfei
Guo
,
Weizhen
Qi
,
Ming
Gong
,
Linjun
Shou
,
Daxin
Jiang
,
Guihong
Cao
,
Xiaodong
Fan
,
Ruofei
Zhang
,
Rahul
Agrawal
,
Edward
Cui
,
Sining
Wei
,
Taroon
Bharti
,
Ying
Qiao
,
Jiun-Hung
Chen
,
Winnie
Wu
,
Shuguang
Liu
,
Fan
Yang
,
Daniel
Campos
,
Rangan
Majumder
, and
Ming
Zhou
.
2020
.
XGLUE: A new benchmark datasetfor cross-lingual pre-training, understanding and generation
. In
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
, pages
6008
6018
,
Online
.
Association for Computational Linguistics
.
Xi
Victoria Lin
,
Todor
Mihaylov
,
Mikel
Artetxe
,
Tianlu
Wang
,
Shuohui
Chen
,
Daniel
Simig
,
Myle
Ott
,
Naman
Goyal
,
Shruti
Bhosale
,
Jingfei
Du
,
Ramakanth
Pasunuru
,
Sam
Shleifer
,
Punit Singh
Koura
,
Vishrav
Chaudhary
,
Brian
O’Horo
,
Jeff
Wang
,
Luke
Zettlemoyer
,
Zornitsa
Kozareva
,
Mona
Diab
,
Veselin
Stoyanov
, and
Xian
Li
.
2022
.
Few-shot learning with multilingual language models
.
Pierre
Lison
and
Jörg
Tiedemann
.
2016
.
OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
. In
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
, pages
923
929
,
Portorož, Slovenia
.
European Language Resources Association (ELRA)
.
Pengfei
Liu
,
Weizhe
Yuan
,
Jinlan
Fu
,
Zhengbao
Jiang
,
Hiroaki
Hayashi
, and
Graham
Neubig
.
2023a
.
Pre-Train, prompt, and predict: A systematic survey of prompting methods in natural language processing
.
ACM Computing Surveys
,
55
(
9
):
1
35
.
Qi
Liu
,
Matt J.
Kusner
, and
Phil
Blunsom
.
2020a
.
A Survey on Contextual Embeddings
.
Tianyu
Liu
,
Yuchen Eleanor
Jiang
,
Nicholas
Monath
,
Ryan
Cotterell
, and
Mrinmaya
Sachan
.
2022
.
Autoregressive structured prediction with language models
. In
Findings of the Association for Computational Linguistics: EMNLP 2022
, pages
993
1005
,
Abu Dhabi, United Arab Emirates
.
Association for Computational Linguistics
.
Xiao
Liu
,
Yanan
Zheng
,
Zhengxiao
Du
,
Ming
Ding
,
Yujie
Qian
,
Zhilin
Yang
, and
Jie
Tang
.
2023b
.
GPT understands, too
.
AI Open
.
Yinhan
Liu
,
Jiatao
Gu
,
Naman
Goyal
,
Xian
Li
,
Sergey
Edunov
,
Marjan
Ghazvininejad
,
Mike
Lewis
, and
Luke
Zettlemoyer
.
2020b
.
Multilingual denoising pre-training for neural machine translation
.
Transactions of the Association for Computational Linguistics
,
8
:
726
742
.
H.
Mann
and
D.
Whitney
.
1947
.
Controlling the false discovery rate: A practical and powerful approach to multiple testing
.
Annals of Mathematical Statistics
,
18
(
1
):
50
60
.
Louis
Martin
,
Benjamin
Muller
,
Pedro Javier Ortiz
Suárez
,
Yoann
Dupont
,
Laurent
Romary
,
Éric
de la Clergerie
,
Djamé
Seddah
, and
Benoît
Sagot
.
2020
.
CamemBERT: A tasty French language model
. In
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
, pages
7203
7219
,
Online
.
Association for Computational Linguistics
.
Mihai
Masala
,
Stefan
Ruseti
, and
Mihai
Dascalu
.
2020
.
RoBERT – a Romanian BERT model
. In
Proceedings of the 28th International Conference on Computational Linguistics
, pages
6626
6637
,
Barcelona, Spain (Online)
.
International Committee on Computational Linguistics
.
R.
Thomas McCoy
,
Junghyun
Min
, and
Tal
Linzen
.
2020
.
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance
. In
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
, pages
217
227
,
Online
.
Association for Computational Linguistics
.
Sabrina J.
Mielke
,
Zaid
Alyafeai
,
Elizabeth
Salesky
,
Colin
Raffel
,
Manan
Dey
,
Matthias
Gallé
,
Arun
Raja
,
Chenglei
Si
,
Wilson Y.
Lee
,
Benoît
Sagot
, and
Samson
Tan
.
2021
.
Between words and characters: A brief history of open-vocabulary modeling and tokenization in NLP
.
Aakanksha
Naik
,
Abhilasha
Ravichander
,
Norman
Sadeh
,
Carolyn
Rose
, and
Graham
Neubig
.
2018
.
Stress test evaluation for natural language inference
. In
Proceedings of the 27th International Conference on Computational Linguistics
, pages
2340
2353
,
Santa Fe, New Mexico, USA
.
Association for Computational Linguistics
.
Timothy
Niven
and
Hung-Yu
Kao
.
2019
.
Probing neural network comprehension of natural language arguments
. In
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
, pages
4658
4664
,
Florence, Italy
.
Association for Computational Linguistics
.
Joakim
Nivre
,
Marie-Catherine
de Marneffe
,
Filip
Ginter
,
Yoav
Goldberg
,
Jan
Hajič
,
Christopher D.
Manning
,
Ryan
McDonald
,
Slav
Petrov
,
Sampo
Pyysalo
,
Natalia
Silveira
,
Reut
Tsarfaty
, and
Daniel
Zeman
.
2016
.
Universal Dependencies v1: A multilingual treebank collection
. In
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
, pages
1659
1666
,
Portorož, Slovenia
.
European Language Resources Association (ELRA)
.
Rodrigo
Nogueira
,
Zhiying
Jiang
, and
Jimmy
Lin
.
2021
.
Investigating the limitations of transformers with simple arithmetic tasks
.
Boris
Orekhov
,
I.
Krylova
,
I.
Popov
,
E.
Stepanova
, and
L.
Zaydelman
.
2016
.
Russian minority languages on the web: Descriptive statistics
. In
Vladimir Selegey (chief ed.), Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue”
, pages
498
508
.
Pedro Javier Ortiz
Suárez
,
Benoît
Sagot
, and
Laurent
Romary
.
2019
.
Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures
.
Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019
, pages
9
16
,
Mannheim
.
Leibniz-Institut für Deutsche Sprache
.
Myle
Ott
,
Sergey
Edunov
,
Alexei
Baevski
,
Angela
Fan
,
Sam
Gross
,
Nathan
Ng
,
David
Grangier
, and
Michael
Auli
.
2019
.
fairseq: A fast, extensible toolkit for sequence modeling
. In
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
, pages
48
53
,
Minneapolis, Minnesota
.
Association for Computational Linguistics
.
David
Paper
.
2021
.
TensorFlow datasets
.
State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem
, pages
65
91
.
Karl
Pearson
.
1895
.
Note on regression and inheritance in the case of two parents
.
Proceedings of the Royal Society of London
,
58
(
347-352
):
240
242
.
Ethan
Perez
,
Douwe
Kiela
, and
Kyunghyun
Cho
.
2021
.
True few-shot learning with language models
.
Advances in Neural Information Processing Systems
,
34
.
Fabio
Petroni
,
Tim
Rocktäschel
,
Sebastian
Riedel
,
Patrick
Lewis
,
Anton
Bakhtin
,
Yuxiang
Wu
, and
Alexander
Miller
.
2019
.
Language models as knowledge bases?
In
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
, pages
2463
2473
,
Hong Kong, China
.
Association for Computational Linguistics
.
Jonas
Pfeiffer
,
Ivan
Vulić
,
Iryna
Gurevych
, and
Sebastian
Ruder
.
2021
.
UNKs everywhere: Adapting multilingual language models to new scripts
. In
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
, pages
10186
10203
,
Online and Punta Cana, Dominican Republic
.
Association for Computational Linguistics
.
Edoardo Maria
Ponti
,
Goran
Glavaš
,
Olga
Majewska
,
Qianchu
Liu
,
Ivan
Vulić
, and
Anna
Korhonen
.
2020
.
XCOPA: A multilingual dataset for causal commonsense reasoning
. In
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
, pages
2362
2376
,
Online
.
Association for Computational Linguistics
.
Alec
Radford
,
Jeffrey
Wu
,
Rewon
Child
,
David
Luan
,
Dario
Amodei
, and
Ilya
Sutskever
.
2019
.
Language models are unsupervised multitask learners
.
OpenAI blog
,
1
(
8
):
9
.
Colin
Raffel
,
Noam
Shazeer
,
Adam
Roberts
,
Katherine
Lee
,
Sharan
Narang
,
Michael
Matena
,
Yanqi
Zhou
,
Wei
Li
, and
Peter J.
Liu
.
2020
.
Exploring the limits of transfer learning with a unified text-to-text transformer
.
Journal of Machine Learning Research
,
21
:
1
67
.
Jeff
Rasley
,
Samyam
Rajbhandari
,
Olatunji
Ruwase
, and
Yuxiong
He
.
2020
.
DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters
. In
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
, pages
3505
3506
.
Phillip
Rust
,
Jonas
Pfeiffer
,
Ivan
Vulić
,
Sebastian
Ruder
, and
Iryna
Gurevych
.
2021
.
How good is your tokenizer? On the monolingual performance of multilingual language models
. In
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
, pages
3118
3135
,
Online
.
Association for Computational Linguistics
.
Teven Le
Scao
,
Angela
Fan
,
Christopher
Akiki
,
Ellie
Pavlick
,
Suzana
Ilić
,
Daniel
Hesslow
,
Roman
Castagné
,
Alexandra Sasha
Luccioni
,
François
Yvon
,
Matthias
Gallé
,
Jonathan
Tow
,
Alexander M.
Rush
,
Stella
Biderman
,
Albert
Webson
,
Pawan Sasanka
Ammanamanchi
,
Thomas
Wang
,
Benoît
Sagot
,
Niklas
Muennighoff
,
Albert Villanova
del Moral
,
Olatunji
Ruwase
,
Rachel
Bawden
,
Stas
Bekman
,
Angelina
McMillan-Major
,
Iz
Beltagy
,
Huu
Nguyen
,
Lucile
Saulnier
,
Samson
Tan
,
Pedro Ortiz
Suarez
,
Victor
Sanh
,
Hugo
Laurençon
,
Yacine
Jernite
,
Julien
Launay
,
Margaret
Mitchell
,
Colin
Raffel
,
Aaron
Gokaslan
,
Adi
Simhi
,
Aitor
Soroa
,
Alham Fikri
Aji
,
Amit
Alfassy
,
Anna
Rogers
,
Ariel Kreisberg
Nitzav
,
Canwen
Xu
,
Chenghao
Mou
,
Chris
Emezue
,
Christopher
Klamm
,
Colin
Leong
,
Daniel
van Strien
,
David Ifeoluwa
Adelani
,
Dragomir
Radev
,
Eduardo González
Ponferrada
,
Efrat
Levkovizh
,
Ethan
Kim
,
Eyal Bar
Natan
,
Francesco
De Toni
,
Gérard
Dupont
,
Germán
Kruszewski
,
Giada
Pistilli
,
Hady
Elsahar
,
Hamza
Benyamina
,
Hieu
Tran
,
Ian
Yu
,
Idris
Abdulmumin
,
Isaac
Johnson
,
Itziar
Gonzalez-Dios
,
Javier
de la Rosa
,
Jenny
Chim
,
Jesse
Dodge
,
Jian
Zhu
,
Jonathan
Chang
,
Jörg
Frohberg
,
Joseph
Tobing
,
Joydeep
Bhattacharjee
,
Khalid
Almubarak
,
Kimbo
Chen
,
Kyle
Lo
,
Leandro
Von Werra
,
Leon
Weber
,
Long
Phan
,
Loubna Ben
allal
,
Ludovic
Tanguy
,
Manan
Dey
,
Manuel Romero
Muñoz
,
Maraim
Masoud
,
María
Grandury
,
Mario
Šaško
,
Max
Huang
,
Maximin
Coavoux
,
Mayank
Singh
,
Mike Tian-Jian
Jiang
,
Minh Chien
Vu
,
Mohammad A.
Jauhar
,
Mustafa
Ghaleb
,
Nishant
Subramani
,
Nora
Kassner
,
Nurulaqilla
Khamis
,
Olivier
Nguyen
,
Omar
Espejel
,
Ona
de Gibert
,
Paulo
Villegas
,
Peter
Henderson
,
Pierre
Colombo
,
Priscilla
Amuok
,
Quentin
Lhoest
,
Rheza
Harliman
,
Rishi
Bommasani
,
Roberto Luis
López
,
Rui
Ribeiro
,
Salomey
Osei
,
Sampo
Pyysalo
,
Sebastian
Nagel
,
Shamik
Bose
,
Shamsuddeen Hassan
Muhammad
,
Shanya
Sharma
,
Shayne
Longpre
,
Somaieh
Nikpoor
,
Stanislav
Silberberg
,
Suhas
Pai
,
Sydney
Zink
,
Tiago Timponi
Torrent
,
Timo
Schick
,
Tristan
Thrush
,
Valentin
Danchev
,
Vassilina
Nikoulina
,
Veronika
Laippala
,
Violette
Lepercq
,
Vrinda
Prabhu
,
Zaid
Alyafeai
,
Zeerak
Talat
,
Arun
Raja
,
Benjamin
Heinzerling
,
Chenglei
Si
,
Davut Emre
Taşar
,
Elizabeth
Salesky
,
Sabrina J.
Mielke
,
Wilson Y.
Lee
,
Abheesht
Sharma
,
Andrea
Santilli
,
Antoine
Chaffin
,
Arnaud
Stiegler
,
Debajyoti
Datta
,
Eliza
Szczechla
,
Gunjan
Chhablani
,
Han
Wang
,
Harshit
Pandey
,
Hendrik
Strobelt
,
Jason Alan
Fries
,
Jos
Rozen
,
Leo
Gao
,
Lintang
Sutawika
,
M
Saiful Bari
,
Maged S.
Alshaibani
,
Matteo
Manica
,
Nihal
Nayak
,
Ryan
Teehan
,
Samuel
Albanie
,
Sheng
Shen
,
Srulik
Ben-David
,
Stephen H.
Bach
,
Taewoon
Kim
,
Tali
Bers
,
Thibault
Fevry
,
Trishala
Neeraj
,
Urmish
Thakker
,
Vikas
Raunak
,
Xiangru
Tang
,
Zheng-Xin
Yong
,
Zhiqing
Sun
,
Shaked
Brody
,
Yallow
Uri
,
Hadar
Tojarieh
,
Adam
Roberts
,
Hyung Won
Chung
,
Jaesung
Tae
,
Jason
Phang
,
Ofir
Press
,
Conglong
Li
,
Deepak
Narayanan
,
Hatim
Bourfoune
,
Jared
Casper
,
Jeff
Rasley
,
Max
Ryabinin
,
Mayank
Mishra
,
Minjia
Zhang
,
Mohammad
Shoeybi
,
Myriam
Peyrounette
,
Nicolas
Patry
,
Nouamane
Tazi
,
Omar
Sanseviero
,
Patrick
von Platen
,
Pierre
Cornette
,
Pierre François
Lavallée
,
Rémi
Lacroix
,
Samyam
Rajbhandari
,
Sanchit
Gandhi
,
Shaden
Smith
,
Stéphane
Requena
,
Suraj
Patil
,
Tim
Dettmers
,
Ahmed
Baruwa
,
Amanpreet
Singh
,
Anastasia
Cheveleva
,
Anne-Laure
Ligozat
,
Arjun
Subramonian
,
Aurélie
Névéol
,
Charles
Lovering
,
Dan
Garrette
,
Deepak
Tunuguntla
,
Ehud
Reiter
,
Ekaterina
Taktasheva
,
Ekaterina
Voloshina
,
Eli
Bogdanov
,
Genta Indra
Winata
,
Hailey
Schoelkopf
,
Jan-Christoph
Kalo
,
Jekaterina
Novikova
,
Jessica Zosa
Forde
,
Jordan
Clive
,
Jungo
Kasai
,
Ken
Kawamura
,
Liam
Hazan
,
Marine
Carpuat
,
Miruna
Clinciu
,
Najoung
Kim
,
Newton
Cheng
,
Oleg
Serikov
,
Omer
Antverg
,
Oskar
van der Wal
,
Rui
Zhang
,
Ruochen
Zhang
,
Sebastian
Gehrmann
,
Shachar
Mirkin
,
Shani
Pais
,
Tatiana
Shavrina
,
Thomas
Scialom
,
Tian
Yun
,
Tomasz
Limisiewicz
,
Verena
Rieser
,
Vitaly
Protasov
,
Vladislav
Mikhailov
,
Yada
Pruksachatkun
,
Yonatan
Belinkov
,
Zachary
Bamberger
,
Zdeněk
Kasner
,
Alice
Rueda
,
Amanda
Pestana
,
Amir
Feizpour
,
Ammar
Khan
,
Amy
Faranak
,
Ana
Santos
,
Anthony
Hevia
,
Antigona
Unldreaj
,
Arash
Aghagol
,
Arezoo
Abdollahi
,
Aycha
Tammour
,
Azadeh
HajiHosseini
,
Bahareh
Behroozi
,
Benjamin
Ajibade
,
Bharat
Saxena
,
Carlos Muñoz
Ferrandis
,
Danish
Contractor
,
David
Lansky
,
Davis
David
,
Douwe
Kiela
,
Duong A.
Nguyen
,
Edward
Tan
,
Emi
Baylor
,
Ezinwanne
Ozoani
,
Fatima
Mirza
,
Frankline
Ononiwu
,
Habib
Rezanejad
,
Hessie
Jones
,
Indrani
Bhattacharya
,
Irene
Solaiman
,
Irina
Sedenko
,
Isar
Nejadgholi
,
Jesse
Passmore
,
Josh
Seltzer
,
Julio Bonis
Sanz
,
Livia
Dutra
,
Mairon
Samagaio
,
Maraim
Elbadri
,
Margot
Mieskes
,
Marissa
Gerchick
,
Martha
Akinlolu
,
Michael
McKenna
,
Mike
Qiu
,
Muhammed
Ghauri
,
Mykola
Burynok
,
Nafis
Abrar
,
Nazneen
Rajani
,
Nour
Elkott
,
Nour
Fahmy
,
Olanrewaju
Samuel
,
Ran
An
,
Rasmus
Kromann
,
Ryan
Hao
,
Samira
Alizadeh
,
Sarmad
Shubber
,
Silas
Wang
,
Sourav
Roy
,
Sylvain
Viguier
,
Thanh
Le
,
Tobi
Oyebade
,
Trieu
Le
,
Yoyo
Yang
,
Zach
Nguyen
,
Abhinav Ramesh
Kashyap
,
Alfredo
Palasciano
,
Alison
Callahan
,
Anima
Shukla
,
Antonio
Miranda-Escalada
,
Ayush
Singh
,
Benjamin
Beilharz
,
Bo
Wang
,
Caio
Brito
,
Chenxi
Zhou
,
Chirag
Jain
,
Chuxin
Xu
,
Clémentine
Fourrier
,
Daniel León
Periñán
,
Daniel
Molano
,
Dian
Yu
,
Enrique
Manjavacas
,
Fabio
Barth
,
Florian
Fuhrimann
,
Gabriel
Altay
,
Giyaseddin
Bayrak
,
Gully
Burns
,
Helena U.
Vrabec
,
Imane
Bello
,
Ishani
Dash
,
Jihyun
Kang
,
John
Giorgi
,
Jonas
Golde
,
Jose David
Posada
,
Karthik Rangasai
Sivaraman
,
Lokesh
Bulchandani
,
Lu
Liu
,
Luisa
Shinzato
,
Madeleine Hahn
de Bykhovetz
,
Maiko
Takeuchi
,
Marc
Pàmies
,
Maria A.
Castillo
,
Marianna
Nezhurina
,
Mario
Sänger
,
Matthias
Samwald
,
Michael
Cullan
,
Michael
Weinberg
,
Michiel
De Wolf
,
Mina
Mihaljcic
,
Minna
Liu
,
Moritz
Freidank
,
Myungsun
Kang
,
Natasha
Seelam
,
Nathan
Dahlberg
,
Nicholas Michio
Broad
,
Nikolaus
Muellner
,
Pascale
Fung
,
Patrick
Haller
,
Ramya
Chandrasekhar
,
Renata
Eisenberg
,
Robert
Martin
,
Rodrigo
Canalli
,
Rosaline
Su
,
Ruisi
Su
,
Samuel
Cahyawijaya
,
Samuele
Garda
,
Shlok S.
Deshmukh
,
Shubhanshu
Mishra
,
Sid
Kiblawi
,
Simon
Ott
,
Sinee
Sang-aroonsiri
,
Srishti
Kumar
,
Stefan
Schweter
,
Sushil
Bharati
,
Tanmay
Laud
,
Théo
Gigant
,
Tomoya
Kainuma
,
Wojciech
Kusa
,
Yanis
Labrak
,
Yash Shailesh
Bajaj
,
Yash
Venkatraman
,
Yifan
Xu
,
Yingxin
Xu
,
Yu
Xu
,
Zhe
Tan
,
Zhongli
Xie
,
Zifan
Ye
,
Mathilde
Bras
,
Younes
Belkada
, and
Thomas
Wolf
.
2023
.
BLOOM: A 176B-parameter open-access multilingual language model
.
Timo
Schick
and
Hinrich
Schütze
.
2021
.
It’s not just size that matters: Small language models are also few-shot learners
. In
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
, pages
2339
2352
,
Online
.
Association for Computational Linguistics
.
Mohammad
Shoeybi
,
Mostofa
Patwary
,
Raul
Puri
,
Patrick
LeGresley
,
Jared
Casper
, and
Bryan
Catanzaro
.
2020
.
Megatron-LM: Training multi-billion parameter language models using model parallelism
.
Emma
Strubell
,
Ananya
Ganesh
, and
Andrew
McCallum
.
2019
.
Energy and policy considerations for deep learning in NLP
. In
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
, pages
3645
3650
,
Florence, Italy
.
Association for Computational Linguistics
.
Alexey
Tikhonov
and
Max
Ryabinin
.
2021
.
It’s all in the heads: Using attention heads as a baseline for cross-lingual transfer in commonsense reasoning
. In
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
, pages
3534
3546
,
Online
.
Association for Computational Linguistics
.
Ashish
Vaswani
,
Noam
Shazeer
,
Niki
Parmar
,
Jakob
Uszkoreit
,
Llion
Jones
,
Aidan N.
Gomez
,
Łukasz
Kaiser
, and
Illia
Polosukhin
.
2017
.
Attention is all you need
.
Advances in Neural Information Processing Systems
,
30
.
Sanh
Victor
,
Webson
Albert
,
Raffel
Colin
,
Bach
Stephen
,
Sutawika
Lintang
,
Alyafeai
Zaid
,
Chaffin
Antoine
,
Stiegler
Arnaud
,
Raja
Arun
,
Dey
Manan
, et al.
2022
.
Multitask prompted training enables zero-shot task generalization
. In
International Conference on Learning Representations
.
Alex
Wang
,
Yada
Pruksachatkun
,
Nikita
Nangia
,
Amanpreet
Singh
,
Julian
Michael
,
Felix
Hill
,
Omer
Levy
, and
Samuel
Bowman
.
2019
.
SuperGLUE: A stickier benchmark for general-purpose language understanding systems
.
Advances in Neural Information Processing Systems
,
32
.
Ben
Wang
and
Aran
Komatsuzaki
.
2021
.
GPT-J-6B: A 6 billion parameter autoregressive language model
.
Changhan
Wang
,
Kyunghyun
Cho
, and
Jiatao
Gu
.
2020
.
Neural machine translation with byte-level subwords
. In
Proceedings of the AAAI Conference on Artificial Intelligence
, volume
34
, pages
9154
9160
.
Shuohang
Wang
,
Yang
Liu
,
Yichong
Xu
,
Chenguang
Zhu
, and
Michael
Zeng
.
2021
.
Want to reduce labeling cost? GPT-3 can help
. In
Findings of the Association for Computational Linguistics: EMNLP 2021
, pages
4195
4205
,
Punta Cana, Dominican Republic
.
Association for Computational Linguistics
.
Laura
Weidinger
,
Jonathan
Uesato
,
Maribeth
Rauh
,
Conor
Griffin
,
Po-Sen
Huang
,
John
Mellor
,
Amelia
Glaese
,
Myra
Cheng
,
Borja
Balle
,
Atoosa
Kasirzadeh
, et al
2022
.
Taxonomy of risks posed by language models
. In
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
, pages
214
229
.
Guillaume
Wenzek
,
Marie-Anne
Lachaux
,
Alexis
Conneau
,
Vishrav
Chaudhary
,
Francisco
Guzmán
,
Armand
Joulin
, and
Edouard
Grave
.
2020
.
CCNet: Extracting high quality monolingual datasets from web crawl data
. In
Proceedings of the 12th Language Resources and Evaluation Conference
, pages
4003
4012
,
Marseille, France
.
European Language Resources Association
.
Genta Indra
Winata
,
Andrea
Madotto
,
Zhaojiang
Lin
,
Rosanne
Liu
,
Jason
Yosinski
, and
Pascale
Fung
.
2021
.
Language models are few-shot multilingual learners
. In
Proceedings of the 1st Workshop on Multilingual Representation Learning
, pages
1
15
,
Punta Cana, Dominican Republic
.
Association for Computational Linguistics
.
Thomas
Wolf
,
Lysandre
Debut
,
Victor
Sanh
,
Julien
Chaumond
,
Clement
Delangue
,
Anthony
Moi
,
Pierric
Cistac
,
Tim
Rault
,
Remi
Louf
,
Morgan
Funtowicz
,
Joe
Davison
,
Sam
Shleifer
,
Patrick
von Platen
,
Clara
Ma
,
Yacine
Jernite
,
Julien
Plu
,
Canwen
Xu
,
Teven Le
Scao
,
Sylvain
Gugger
,
Mariama
Drame
,
Quentin
Lhoest
, and
Alexander
Rush
.
2020
.
Transformers: State-of-the-art natural language processing
. In
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
, pages
38
45
,
Online
.
Association for Computational Linguistics
.
Shijie
Wu
and
Mark
Dredze
.
2020
.
Are all languages created equal in multilingual BERT?
In
Proceedings of the 5th Workshop on Representation Learning for NLP
, pages
120
130
,
Online
.
Association for Computational Linguistics
.
Sang Michael
Xie
,
Hieu
Pham
,
Xuanyi
Dong
,
Nan
Du
,
Hanxiao
Liu
,
Yifeng
Lu
,
Percy
Liang
,
Quoc V.
Le
,
Tengyu
Ma
, and
Adams Wei
Yu
.
2023
.
DoReMi: Optimizing data mixtures speeds up language model pretraining
.
Linting
Xue
,
Noah
Constant
,
Adam
Roberts
,
Mihir
Kale
,
Rami
Al-Rfou
,
Aditya
Siddhant
,
Aditya
Barua
, and
Colin
Raffel
.
2021
.
mT5: A massively multilingual pre-trained text-to-text transformer
. In
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
, pages
483
498
,
Online
.
Association for Computational Linguistics
.
Yinfei
Yang
,
Yuan
Zhang
,
Chris
Tar
, and
Jason
Baldridge
.
2019a
.
PAWS-X: A cross-lingual adversarial dataset for paraphrase identification
. In
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
, pages
3687
3692
,
Hong Kong, China
.
Association for Computational Linguistics
.
Zhilin
Yang
,
Zihang
Dai
,
Yiming
Yang
,
Jaime
Carbonell
,
Russ R.
Salakhutdinov
, and
Quoc V.
Le
.
2019b
.
XLNet: Generalized autoregressive pretraining for language understanding
.
Advances in Neural Information Processing Systems
,
32
.
Wei
Zeng
,
Xiaozhe
Ren
,
Teng
Su
,
Hui
Wang
,
Yi
Liao
,
Zhiwei
Wang
,
Xin
Jiang
,
ZhenZhang
Yang
,
Kaisheng
Wang
,
Xiaoda
Zhang
,
Chen
Li
,
Ziyan
Gong
,
Yifan
Yao
,
Xinjing
Huang
,
Jun
Wang
,
Jianfeng
Yu
,
Qi
Guo
,
Yue
Yu
,
Yan
Zhang
,
Jin
Wang
,
Hengtao
Tao
,
Dasen
Yan
,
Zexuan
Yi
,
Fang
Peng
,
Fangqing
Jiang
,
Han
Zhang
,
Lingfeng
Deng
,
Yehong
Zhang
,
Zhe
Lin
,
Chao
Zhang
,
Shaojie
Zhang
,
Mingyue
Guo
,
Shanzhi
Gu
,
Gaojun
Fan
,
Yaowei
Wang
,
Xuefeng
Jin
,
Qun
Liu
, and
Yonghong
Tian
.
2021
.
PanGu-α: Large-scale autoregressive pretrained chinese language models with auto-parallel computation
.
Susan
Zhang
,
Stephen
Roller
,
Naman
Goyal
,
Mikel
Artetxe
,
Moya
Chen
,
Shuohui
Chen
,
Christopher
Dewan
,
Mona
Diab
,
Xian
Li
,
Xi
Victoria Lin
,
Todor
Mihaylov
,
Myle
Ott
,
Sam
Shleifer
,
Kurt
Shuster
,
Daniel
Simig
,
Punit Singh
Koura
,
Anjali
Sridhar
,
Tianlu
Wang
, and
Luke
Zettlemoyer
.
2022
.
OPT: Open pre-trained transformer language models
.
Zihao
Zhao
,
Eric
Wallace
,
Shi
Feng
,
Dan
Klein
, and
Sameer
Singh
.
2021
.
Calibrate before use: Improving few-shot performance of language models
. In
International Conference on Machine Learning
, pages
12697
12706
.
PMLR
.
Dmitry
Zmitrovich
,
Alexander
Abramov
,
Andrey
Kalmykov
,
Maria
Tikhonova
,
Ekaterina
Taktasheva
,
Danil
Astafurov
,
Mark
Baushenko
,
Artem
Snegirev
,
Tatiana
Shavrina
,
Sergey
Markov
,
Vladislav
Mikhailov
, and
Alena
Fenogenova
.
2023
.
A family of pretrained transformer language models for Russian
.

Author notes

*

Work done while at SaluteDevices.

Now at University of Oslo.

Action Editor: Miguel Ballesteros

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