Fetching the paper…
Reading the bibliography…
Large language models can perform various reasoning tasks by using chain-of-thought prompting, which guides them to find answers through step-by-step demonstrations.
MAWPS: A math word problem repository
Koncel-Kedziorski, R., Roy, S., Amini, A., Kushman, N., and Hajishirzi, H · 2016
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Reimers, N. and Gurevych, I · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., 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. M., 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
Earlier work this paper cites.
The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
Earlier work this paper cites.
A diverse corpus for evaluating and developing english math word problem solvers
Miao, S., Liang, C., and Su, K · 2020
Earlier work this paper cites.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
Earlier work this paper cites.
Are NLP models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
Earlier work this paper cites.
Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
Earlier work this paper cites.
Palm: Scaling language modeling with pathways
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
Earlier work this paper cites.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Narang, S., Mishra, G., Yu, A., Zhao, V. Y., Huang, Y., Dai, A. M., Yu, H., Petrov, S., Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
Earlier work this paper cites.
Compositional semantic parsing with large language models
Drozdov, A., Schärli, N., Akyürek, E., Scales, N., Song, X., Chen, X., Bousquet, O., and Zhou, D · 2022
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T · 2022
Cited alongside, same era.
Large language models can self-improve
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 2022
Cited alongside, same era.
Decomposed prompting: A modular approach for solving complex tasks
Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., and Sabharwal, A · 2022
Cited alongside, same era.
Measuring and narrowing the compositionality gap in language models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., and Lewis, M · 2022
Later among the works it cites.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Raja, A., Dey, M., Bari, M. S., Xu, C., Thakker, U., Sharma, S. S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N. V., Datta, D., Chang, J., Jiang, M. T., Wang, H., Manica, M., Shen, S., Yong, Z. X., Pandey, H., Bawden, R., Wang, T., Neeraj, T., Rozen, J., Sharma, A., Santilli, A., Févry, T., Fries, J. A., Teehan, R., Scao, T. L., Biderman, S., Gao, L., Wolf, T., and Rush, A. M · 2022
Later among the works it cites.
On the effect of pretraining corpora on in-context learning by a large-scale language model
Shin, S., Lee, S., Ahn, H., Kim, S., Kim, H., Kim, B., Cho, K., Lee, G., Park, W., Ha, J., and Sung, N · 2022
Later among the works it cites.
Challenging big-bench tasks and whether chain-of-thought can solve them
Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E. H., Zhou, D., and Wei, J · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Cited alongside, same era.
On the advance of making language models better reasoners
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., and Chen, W · 2022
Cited alongside, same era.
What makes good in-context examples for gpt-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2022
Cited alongside, same era.
Z-ICL: zero-shot in-context learning with pseudo-demonstrations
Lyu, X., Min, S., Beltagy, I., Zettlemoyer, L., and Hajishirzi, H · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
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. F., Leike, J., and Lowe, R · 2022
Cited alongside, same era.
Reasoning like program executors
Pi, X., Liu, Q., Chen, B., Ziyadi, M., Lin, Z., Gao, Y., Fu, Q., Lou, J., and Chen, W · 2022
Cited alongside, same era.
Rarr: Researching and revising what language models say, using language models, 2022a
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V. Y., Lao, N., Lee, H., Juan, D.-C., and Guu, K
Cited in the paper.
PAL: program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., and Neubig, G
Cited in the paper.
Lamda: Language models for dialog applications
Thoppilan, R., Freitas, D. D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, H. S., Ghafouri, A., Menegali, M., Huang, Y., Krikun, M., Lepikhin, D., Qin, J., Chen, D., Xu, Y., Chen, Z., Roberts, A., Bosma, M., Zhou, Y., Chang, C., Krivokon, I., Rusch, W., Pickett, M., Meier-Hellstern, K. S., Morris, M. R., Doshi, T., Santos, R. D., Duke, T., Soraker, J., Zevenbergen, B., Prabhakaran, V., Diaz, M., Hutchinson, B., Olson, K., Molina, A., Hoffman-John, E., Lee, J., Aroyo, L., Rajakumar, R., Butryna, A., Lamm, M., Kuzmina, V., Fenton, J., Cohen, A., Bernstein, R., Kurzweil, R., Aguera-Arcas, B., Cui, C., Croak, M., Chi, E. H., and Le, Q · 2022
Later among the works it cites.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2022
Later among the works it cites.
Symbolic knowledge distillation: from general language models to commonsense models
West, P., Bhagavatula, C., Hessel, J., Hwang, J. D., Jiang, L., Bras, R. L., Lu, X., Welleck, S., and Choi, Y · 2022
Later among the works it cites.
DOC: improving long story coherence with detailed outline control
Yang, K., Klein, D., Peng, N., and Tian, Y · 2022
Later among the works it cites.
Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Bousquet, O., Le, Q., and Chi, E. H · 2022
Later among the works it cites.