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Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) based on a few examples presented in dialogue history without any model parameter update.
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Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
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Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers
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In-context demonstration selection with cross entropy difference
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Which examples to annotate for in-context learning? towards effective and efficient selection
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Selective annotation makes language models better few-shot learners
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
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In-context learning with iterative demonstration selection
Chengwei Qin, Aston Zhang, Anirudh Dagar, and Wenming Ye. 2023 · 2023
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Robust speech recognition via large-scale weak supervision
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Misconfidence-based demonstration selection for llm in-context learning
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