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Julian Martin Eisenschlos
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Journal Articles
Publisher: Journals Gateway
Transactions of the Association for Computational Linguistics (2025) 13: 461–480.
Published: 06 June 2025
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View articletitled, TANQ: An Open Domain Dataset of Table Answered Questions
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for article titled, TANQ: An Open Domain Dataset of Table Answered Questions
Language models, potentially augmented with tool usage such as retrieval, are becoming the go-to means of answering questions. Understanding and answering questions in real-world settings often requires retrieving information from different sources, processing and aggregating data to extract insights, and presenting complex findings in form of structured artifacts such as novel tables, charts, or infographics. In this paper, we introduce TANQ, 1 the first open-domain question answering dataset where the answers require building tables from information across multiple sources. We release the full source attribution for every cell in the resulting table and benchmark state-of-the-art language models in open, oracle, and closed book setups. Our best-performing baseline, Gemini Flash, reaches an overall F1 score of 60.7, lagging behind human performance by 12.3 points. We analyze baselines’ performance across different dataset attributes such as different skills required for this task, including multi-hop reasoning, math operations, and unit conversions. We further discuss common failures in model-generated answers, suggesting that TANQ is a complex task with many challenges ahead.
Journal Articles
Publisher: Journals Gateway
Transactions of the Association for Computational Linguistics (2022) 10: 257–273.
Published: 18 March 2022
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View articletitled, Time-Aware Language Models as Temporal Knowledge Bases
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for article titled, Time-Aware Language Models as Temporal Knowledge Bases
Many facts come with an expiration date, from the name of the President to the basketball team Lebron James plays for. However, most language models (LMs) are trained on snapshots of data collected at a specific moment in time. This can limit their utility, especially in the closed-book setting where the pretraining corpus must contain the facts the model should memorize. We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum—those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data. To mitigate these problems, we propose a simple technique for jointly modeling text with its timestamp. This improves memorization of seen facts from the training time period, as well as calibration on predictions about unseen facts from future time periods. We also show that models trained with temporal context can be efficiently “refreshed” as new data arrives, without the need for retraining from scratch.