PRover: Proof Generation for Interpretable Reasoning over Rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, Mohit Bansal
Abstract
Recent work by Clark et al. (2020) shows that transformers can act as "soft theorem provers" by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PROVER, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. During inference, a valid proof, satisfying a set of global constraints is generated. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance. First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation). Second, when trained on questions requiring lower depths of reasoning, it generalizes significantly better to higher depths (up to 15% improvement). Third, PROVER obtains near perfect QA accuracy of 98% using only 40% of the training data. However, generating proofs for questions requiring higher depths of reasoning becomes challenging, and the accuracy drops to 65% for "depth 5", indicating significant scope for future work. 1 Facts : F 1 : The bald eagle eats the lion. F2: The bald eagle sees the tiger. F3: The lion chases the bald eagle. F 4 : The lion eats the mouse. F5: The mouse eats the tiger. F6: The tiger eats the bald eagle. F 7 : The tiger is red. Rules : R1: If the lion is green and the lion is not kind then the lion sees the bald eagle. R2: If someone sees the lion then they eat the mouse. R 3 : If someone is kind and not green then they see the bald eagle. R4: If someone is rough then they see the lion. R5: If someone sees the lion and they do not eat the tiger then the tiger is rough. R 6 : If someone eats the bald eagle and the bald eagle is not kind then the bald eagle is rough. R7: If someone does not eat the lion then the lion is big. R8: If someone is kind then they do not eat the mouse. Q4: The bald eagle eats the mouse. [ Answer : T ] Q5: The tiger does not eat the mouse.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext d1153b69-08e2-4309-b663-71ee1befe904Cited by top-tier papers20
- Selection-Inference: Exploiting Large Language Models for Interpretable Logical ReasoningAntonia Creswell, Murray Shanahan, Irina HigginsICLR 2023 · 110 citations
- NaturalProver: Grounded Mathematical Proof Generation with Language ModelsSean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi et al.NeurIPS 2022 · 108 citations
- Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic CorpusTerufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro SogawaNeurIPS 2024 · 60 citations
- FaiRR: Faithful and Robust Deductive Reasoning over Natural LanguageSoumya Sanyal, Harman Singh, Xiang RenACL 2022 · 49 citations
- Pushing the Limits of Rule Reasoning in Transformers through Natural Language SatisfiabilityKyle Richardson, Ashish SabharwalAAAI 2022 · 29 citations
Builds on5
- Deep Learning For Symbolic MathematicsGuillaume Lample, François ChartonICLR 2020 · 477 citations
- Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?Peter Hase, Mohit BansalACL 2020 · 216 citations
- Probing Natural Language Inference Models through Semantic FragmentsKyle Richardson, Hai Hu, Lawrence S. Moss, Ashish SabharwalAAAI 2020 · 152 citations
- WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema ChallengeHongming Zhang, Xinran Zhao, Yangqiu SongACL 2020 · 33 citations
- Learning to Reason: Leveraging Neural Networks for Approximate DNF CountingRalph Abboud, Ismail Ilkan Ceylan, Thomas LukasiewiczAAAI 2020 · 32 citations
Related papers
- Measuring Systematic Generalization in Neural Proof Generation with TransformersNicolas Gontier, Koustuv Sinha, Siva Reddy, Christopher PalNeurIPS 2020 · 69 citations
- Proving Theorems using Incremental Learning and Hindsight Experience ReplayEser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot et al.ICML 2022 · 22 citations
- HyperTree Proof Search for Neural Theorem ProvingGuillaume Lample, Timothée Lacroix, Marie-Anne Lachaux, Aurélien Rodriguez et al.NeurIPS 2022 · 271 citations
- Learning Reasoning Strategies in End-to-End Differentiable ProvingPasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette et al.ICML 2020 · 102 citations
- Baldur: Whole-Proof Generation and Repair with Large Language ModelsEmily First, Markus N. Rabe, Talia Ringer, Yuriy BrunFSE 2023 · 89 citations
