🔗 Who cites «WT5?! Training Text-to-Text Models to Explain their Predictions»

100 papers of our library cite it (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · ← back to «WT5?! Training Text-to-Text Models to Explain their Predictions»

1. Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsarXiv:2201.11903Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.2022 · 11,499 citations
2. mT5: A Massively Multilingual Pre-trained Text-to-Text TransformerConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Linting Xue, Noah Constant, Adam Roberts, et al.2021 · 3,226 citations
3. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model SizesFindings of the Association for Computational LinguisticsCheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.2023 · 690 citations
4. Large Language Models Can Self-ImproveConference on Empirical Methods in Natural Language Processing (EMNLP)Jiaxin Huang, Shixiang Gu, Le Hou, et al.2023 · 575 citations
5. ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural LanguageFindings of the Association for Computational LinguisticsOyvind Tafjord, Bhavana Dalvi, Peter Clark2021 · 335 citations
6. Can language models learn from explanations in context?Findings of the Association for Computational LinguisticsAndrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.2022 · 295 citations
7. Measuring Association Between Labels and Free-Text RationalesConference on Empirical Methods in Natural Language Processing (EMNLP)Sarah Wiegreffe, Ana Marasović, Noah A. Smith2021 · 220 citations
8. Maieutic Prompting: Logically Consistent Reasoning with Recursive ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP)Jaehun Jung, Lianhui Qin, Sean Welleck, et al.2022 · 219 citations
9. Teaching language models to support answers with verified quotesarXiv:2203.11147Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.2022 · 218 citations
10. Towards Faithful Model Explanation in NLP: A SurveyComputational LinguisticsQing Lyu, Marianna Apidianaki, Chris Callison-Burch2024 · 197 citations
11. Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceAntoine Louis, Gijs van Dijck, Gerasimos Spanakis2023 · 157 citations
12. Explaining NLP Models via Minimal Contrastive Editing (MiCE)Findings of the Association for Computational LinguisticsAlexis Ross, Ana Marasović, Matthew Peters2021 · 155 citations
13. A Review on Language Models as Knowledge BasesarXiv:2204.06031Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.2022 · 150 citations
14. Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-StepAnnual Meeting of the Association for Computational Linguistics (ACL)Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.2023 · 140 citations
15. Few-Shot Self-Rationalization with Natural Language PromptsFindings of the Association for Computational LinguisticsAna Marasovic, Iz Beltagy, Doug Downey, et al.2022 · 134 citations
16. FLUTE: Figurative Language Understanding through Textual ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP)Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.2022 · 133 citations
17. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?Findings of the Association for Computational LinguisticsPeter Hase, Shiyue Zhang, Harry Xie, et al.2020 · 126 citations
18. Explanations from Large Language Models Make Small Reasoners BetterarXiv:2210.06726Shiyang Li, Jianshu Chen, Yelong Shen, et al.2022 · 120 citations
19. Reframing Human-AI Collaboration for Generating Free-Text ExplanationsarXiv:2112.08674Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, et al.2021 · 111 citations
20. Rationale-Augmented Ensembles in Language ModelsarXiv:2207.00747Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.2022 · 101 citations
21. Faithfulness Tests for Natural Language ExplanationsAnnual Meeting of the Association for Computational Linguistics (ACL)Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, et al.2023 · 96 citations
22. QED: A Framework and Dataset for Explanations in Question AnsweringTransactions of the Association for Computational Linguistics (TACL)Matthew Lamm, Jennimaria Palomaki, Chris Alberti, et al.2021 · 91 citations
23. Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge DistillationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Abhijnan Nath, Shadi Manafi Avari, Avyakta Chelle, et al.2024 · 16 citations
24. Prompting for explanations improves Adversarial NLI. Is this true? \Yes\ it is \true\ because ıt weakens superficial cues\Findings of the Association for Computational LinguisticsPride Kavumba, Ana Brassard, Benjamin Heinzerling, et al.2023 · 14 citations
25. Tailoring Self-Rationalizers with Multi-Reward DistillationInternational Conference on Learning Representations (ICLR)Sahana Ramnath, Brihi Joshi, Skyler Hallinan, et al.2023 · 14 citations
26. Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human RatingsBlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLPHendrik Schuff, Hsiu-Yu Yang, Heike Adel, et al.2021 · 11 citations
27. Understanding and Predicting Human Label Variation in Natural Language Inference through ExplanationarXiv:2304.12443Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe2023 · 8 citations

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