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We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods.
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Attention is all you need
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Language models are few-shot learners
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Show your work: Scratchpads for intermediate computation with language models
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Finetuned language models are zero-shot learners
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Palm: Scaling language modeling with pathways
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Scaling instruction-finetuned language models
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Compositional semantic parsing with large language models
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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
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Less is more: Summary of long instructions is better for program synthesis
Kuznia, K., Mishra, S., Parmar, M., and Baral, C · 2022
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Lila: A unified benchmark for mathematical reasoning
Mishra, S., Finlayson, M., Lu, P., Tang, L., Welleck, S., Baral, C., Rajpurohit, T., Tafjord, O., Sabharwal, A., Clark, P., et al · 2022
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Reframing instructional prompts to gptk’s language
Mishra, S., Khashabi, D., Baral, C., Choi, Y., and Hajishirzi, H · 2022
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Chatgpt: Optimizing language models for dialogue, 2022
OpenAI · 2022
Skills-in-context prompting: Unlocking compositionality in large language models
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Promptbreeder: Self-referential self-improvement via prompt evolution
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Reasoning with language model is planning with world model
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Plan, verify and switch: Integrated reasoning with diverse x-of-thoughts
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Is a question decomposition unit all we need?
Patel, P., Mishra, S., Parmar, M., and Baral, C · 2022
Cited alongside, same era.
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., et al · 2022
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., 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., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
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Mishra, S. and Nouri, E · 2023
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Branch-solve-merge improves large language model evaluation and generation
Saha, S., Levy, O., Celikyilmaz, A., Bansal, M., Weston, J., and Li, X · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Llama 2: Open foundation and fine-tuned chat models
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Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., Lee, R. K.-W., and Lim, E.-P · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
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Large language models as analogical reasoners
Yasunaga, M., Chen, X., Li, Y., Pasupat, P., Leskovec, J., Liang, P., Chi, E. H., and Zhou, D · 2023
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Take a step back: Evoking reasoning via abstraction in large language models
Zheng, H. S., Mishra, S., Chen, X., Cheng, H.-T., Chi, E. H., Le, Q. V., and Zhou, D · 2023
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How far are large language models from agents with theory-of-mind?
Zhou, P., Madaan, A., Potharaju, S. P., Gupta, A., McKee, K. R., Holtzman, A., Pujara, J., Ren, X., Mishra, S., Nematzadeh, A., et al · 2023
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