Papers › ReasonBERT: Pre-trained to Reason with Distant Supervision
ReasonBERT: Pre-trained to Reason with Distant Supervision
Xiang Deng, Yu Su, Alyssa Lees, You Wu, Cong Yu, Huan Sun
We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBert achieves remarkable improvement over an array of strong baselines. Few-shot experiments further demonstrate that our pre-training method substantially improves sample efficiency.
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Code
Syntology Ran 3 of 11 code samples harvested from 1 repository linked to this paper; 8 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
11 samples harvested; 3 ran; 1 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | TriviaQA | ReasonBERTR | F1 | 45.5 | #55 of 56 | Archive leaderboard | report |
| Question Answering | TriviaQA | ReasonBERTB | F1 | 37.2 | #56 of 56 | Archive leaderboard | report |
| Semantic Parsing | GraphQuestions | ReasonBERTR | F1 Score | 41.3 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: ReasonBERT
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