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Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks.
Multi-hop question answering via reasoning chains
Chen, J., Lin, S., and Durrett, G · 1910
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A learning algorithm for boltzmann machines
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J · 1985
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Wt5?! training text-to-text models to explain their predictions, 2020
Narang, S., Raffel, C., Lee, K., Roberts, A., Fiedel, N., and Malkan, K · 2004
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The pascal recognising textual entailment challenge
Dagan, I., Glickman, O., and Magnini, B · 2005
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The second pascal recognising textual entailment challenge
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., and Szpektor, I · 2006
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The third pascal recognizing textual entailment challenge
Giampiccolo, D., Magnini, B., Dagan, I., and Dolan, B · 2007
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Using “annotator rationales” to improve machine learning for text categorization
Zaidan, O., Eisner, J., and Piatko, C · 2007
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The fifth pascal recognizing textual entailment challenge
Bentivogli, L., Clark, P., Dagan, I., and Giampiccolo, D · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
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Controlling linguistic style aspects in neural language generation
Ficler, J. and Goldberg, Y · 2017
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First quora dataset release: Question pairs, 2017
Iyer, S., Dandekar, N., and Csernai, K · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
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e-snli: Natural language inference with natural language explanations
Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., and Blunsom, P · 2018
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., and Manning, C. D · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Pilehvar, M. T. and Camacho-Collados, J · 2019
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Explain yourself! Leveraging language models for commonsense reasoning
Rajani, N. F., McCann, B., Xiong, C., and Socher, R · 2019
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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Cited alongside, same era.
Learning to retrieve reasoning paths over wikipedia graph for question answering
Asai, A., Hashimoto, K., Hajishirzi, H., Socher, R., and Xiong, C · 2020
Cited alongside, same era.
Promptsource: An integrated development environment and repository for natural language prompts, 2022
Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., Alyafeai, Z., Dey, M., Santilli, A., Sun, Z., Ben-David, S., Xu, C., Chhablani, G., Wang, H., Fries, J. A., Al-shaibani, M. S., Sharma, S., Thakker, U., Almubarak, K., Tang, X., Tang, X., Jiang, M. T.-J., and Rush, A. M · 2022
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Palm: Scaling language modeling with pathways, 2022
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
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Selection-inference: Exploiting large language models for interpretable logical reasoning, 2022
Creswell, A., Shanahan, M., and Higgins, I · 2022
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., and Kiela, D · 2020
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How much knowledge can you pack into the parameters of a language model?
Roberts, A., Raffel, C., and Shazeer, N · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
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Training verifiers to solve math word problems, 2021
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
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Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., and Choi, Y · 2022
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Large language models are zero-shot reasoners, 2022
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Can language models learn from explanations in context?, 2022
Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., and Hill, F · 2022
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On the advance of making language models better reasoners, 2022
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., and Chen, W · 2022
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Generated knowledge prompting for commonsense reasoning
Liu, J., Liu, A., Lu, X., Welleck, S., West, P., Le Bras, R., Choi, Y., and Hajishirzi, H · 2022
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What makes good in-context examples for GPT-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2022
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Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Reasoning like program executors, 2022
Pi, X., Liu, Q., Chen, B., Ziyadi, M., Lin, Z., Gao, Y., Fu, Q., Lou, J.-G., and Chen, W · 2022
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Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al · 2022
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Self-consistency improves chain of thought reasoning in language models, 2022
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D · 2022
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Reframing human-ai collaboration for generating free-text explanations
Wiegreffe, S., Hessel, J., Swayamdipta, S., Riedl, M., and Choi, Y · 2022
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The unreliability of explanations in few-shot in-context learning, 2022
Ye, X. and Durrett, G · 2022
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Star: Bootstrapping reasoning with reasoning, 2022
Zelikman, E., Wu, Y., Mu, J., and Goodman, N. D · 2022
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