Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Aman Madaan
- sl:arxiv_num : 2303.17651
- sl:arxiv_published : 2023-03-30T18:30:01Z
- sl:arxiv_summary : Like people, LLMs do not always generate the best text for a given generation
problem on their first try (e.g., summaries, answers, explanations). Just as
people then refine their text, we introduce SELF-REFINE, a framework for
similarly improving initial outputs from LLMs through iterative feedback and
refinement. The main idea is to generate an output using an LLM, then allow the
same model to provide multi-aspect feedback for its own output; finally, the
same model refines its previously generated output given its own feedback.
Unlike earlier work, our iterative refinement framework does not require
supervised training data or reinforcement learning, and works with a single
LLM. We experiment with 7 diverse tasks, ranging from review rewriting to math
reasoning, demonstrating that our approach outperforms direct generation. In
all tasks, outputs generated with SELF-REFINE are preferred by humans and by
automated metrics over those generated directly with GPT-3.5 and GPT-4,
improving on average by absolute 20% across tasks.@en
- sl:arxiv_title : Self-Refine: Iterative Refinement with Self-Feedback@en
- sl:arxiv_updated : 2023-03-30T18:30:01Z
- sl:bookmarkOf : https://arxiv.org/abs/2303.17651
- sl:creationDate : 2023-04-03
- sl:creationTime : 2023-04-03T07:59:31Z
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