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- W4322760437 abstract "Instruction learning of Large Language Models (LLMs) has enabled zero-shot task generalization. However, instruction learning has been predominantly approached as a fine-tuning problem, including instruction tuning and reinforcement learning from human feedback, where LLMs are multi-task fine-tuned on various tasks with instructions. In this paper, we present a surprising finding that applying in-context learning to instruction learning, referred to as In-Context Instruction Learning (ICIL), significantly improves the zero-shot task generalization performance for both pretrained and instruction-fine-tuned models. One of the core advantages of ICIL is that it uses a single fixed prompt to evaluate all tasks, which is a concatenation of cross-task demonstrations. In particular, we demonstrate that the most powerful instruction-fine-tuned baseline (text-davinci-003) also benefits from ICIL by 9.3%, indicating that the effect of ICIL is complementary to instruction-based fine-tuning." @default.
- W4322760437 created "2023-03-03" @default.
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- W4322760437 date "2023-02-28" @default.
- W4322760437 modified "2023-09-27" @default.
- W4322760437 title "In-Context Instruction Learning" @default.
- W4322760437 doi "https://doi.org/10.48550/arxiv.2302.14691" @default.
- W4322760437 hasPublicationYear "2023" @default.
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