Flexible Generation of Natural Language Deductions
Kaj Bostrom, Xinyu Zhao, Swarat Chaudhuri, Greg Durrett
摘要
An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form. Natural language is an attractive representation for this purpose -it is both highly expressive and easy for humans to understand. However, manipulating natural language statements in logically consistent ways is hard: models must cope with variation in how meaning is expressed while remaining precise. In this paper, we describe PARAPATTERN, a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision. We train BART-based models (Lewis et al., 2020) to generate the result of applying a particular logical operation to one or more premise statements. Crucially, we develop a largely automated pipeline for constructing suitable training examples from Wikipedia. We evaluate our models using out-of-domain sentence compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et al., 2021) datasets as well as targeted perturbation sets. Our results show that our models are substantially more accurate and flexible than baseline systems. PARAPATTERN achieves 85% validity on examples of the 'substitution' operation from EntailmentBank without the use of any in-domain training data, matching the performance of a model fine-tuned for EntailmentBank. The full source code for our method is publicly available. 1 . Substitution Premises: Staphylococcus epidermis is a microorganism. Microorganisms colonize the skin surface. Paraphrased: Staphylococcus epidermidis is a microorganism. Microbiological colonization of the skin surface. "Staphylococcus Epidermidis is a Microorganism." The skin surface is colonized by micro organisms. Conclusion: Staphylococcus epidermis colonizes the skin surface. Premises: During the undergraduate years, seminarians learn the ancient language courses. Latin is an ancient language course. Paraphrased: The seminars know the ancient language courses. Latin is an old language course. Seminarians learn ancient language during their undergraduate years. Latin is a language. Conclusion: During the undergraduate years, seminarians learn Latin. Contraposition Premise: As such, rivers that have headwaters in the mountains provide water for irrigation in the surrounding lands. Paraphrased: In order for water to be used in the surrounding lands, the rivers in the mountains must have their headwaters there. Conclusion: As such, rivers that do not provide water for irrigation in the surrounding lands do not have headwaters in the mountains. Premise: Dogs that are especially dirty or hungry are not able to participate in contests. Paraphrased: To participate in a contest, dogs that are dirty or hungry, must be turned away. Conclusion: Dogs that are able to participate in contests are not especially dirty or hungry. Substitution -Control Premises: RSA is a cryptographic system. Cryptographic systems let people exchange messages securely. Conclusion: RSA lets people exchange messages securely. Predicted: RSA lets people exchange messages securely. Link NP mismatch Premises: RSA is a cryptographic system. Encryption protocols let people exchange messages securely. Conclusion: RSA lets people exchange messages securely. Predicted: RSA allows people to exchange messages securely. Identity VP mismatch Premises: Dominant cryptographic systems include RSA. Cryptographic systems let people exchange messages securely. Conclusion: RSA lets people exchange messages securely. Predicted: RSA allows people to exchange messages securely. NP + VP mismatch Premises: Dominant encryption protocols include RSA. Cryptographic systems let people exchange messages securely. Conclusion: RSA lets people exchange messages securely. Predicted: RSA allows people to exchange messages securely. Number agreement Premises: RSA is a cryptographic system. Cryptographic systems shield web traffic from surveillance and let people communicate securely. Conclusion: RSA shields web traffic from surveillance and lets people communicate securely. Predicted: RSA shields web traffic from surveillance and let people communicate securely.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper14
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma 等NeurIPS 2022 · 被引用 22,562 次
- NaturalProver: Grounded Mathematical Proof Generation with Language ModelsSean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi 等NeurIPS 2022 · 被引用 108 次
- Maieutic Prompting: Logically Consistent Reasoning with Recursive ExplanationsJaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman 等EMNLP 2022 · 被引用 72 次
- Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic CorpusTerufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro SogawaNeurIPS 2024 · 被引用 60 次
- Learning Deductive Reasoning from Synthetic Corpus based on Formal LogicTerufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro SogawaICML 2023 · 被引用 45 次
它引用的顶会 Paper12
- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive SummarizationJingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. LiuICML 2020 · 被引用 2,453 次
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad 等ACL 2020 · 被引用 1,224 次
- (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge GraphsJena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da 等AAAI 2021 · 被引用 458 次
- QASC: A Dataset for Question Answering via Sentence CompositionTushar Khot, Peter Clark, Michal Guerquin, Peter Jansen 等AAAI 2020 · 被引用 387 次
- ReClor: A Reading Comprehension Dataset Requiring Logical ReasoningWeihao Yu, Zihang Jiang, Yanfei Dong, Jiashi FengICLR 2020 · 被引用 325 次
相关 Paper
- Natural Language Deduction with Incomplete InformationZayne Sprague, Kaj Bostrom, Swarat Chaudhuri, Greg DurrettEMNLP 2022 · 被引用 8 次
- PINTO: Faithful Language Reasoning Using Prompt-Generated RationalesPeifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen 等ICLR 2023 · 被引用 21 次
- Enhancing Chain of Thought Prompting in Large Language Models via Reasoning PatternsYufeng Zhang, Xuepeng Wang, Lingxiang Wu, Jinqiao WangAAAI 2025 · 被引用 27 次
- Learning to Selectively Learn for Weakly-supervised Paraphrase GenerationKaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan 等EMNLP 2021 · 被引用 4 次
- Entailment Relation Aware Paraphrase GenerationAbhilasha Sancheti, Balaji Vasan Srinivasan, Rachel RudingerAAAI 2022 · 被引用 5 次
