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The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models (100+ billion parameters).
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Language models are few-shot learners
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How does GPT obtain its ability? tracing emergent abilities of language models to their sources
Fu, Y., Peng, H., and Khot, T · 2022
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Large language models are reasoning teachers
Ho, N., Schmid, L., and Yun, S.-Y · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
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Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 2022
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Opt-iml: Scaling language model instruction meta learning through the lens of generalization
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Explanations from large language models make small reasoners better
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Teaching small language models to reason
Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., and Severyn, A · 2022
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Emergent abilities of large language models
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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
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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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Unifying language learning paradigms
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Copilot explorer
Thakkar, P · 2022
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Lamda: Language models for dialog applications
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., and Zhou, D · 2022
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