ryokamoi/llm-self-correction-papers
List of papers on Self-Correction of LLMs.
This is a curated collection of academic papers focused on how Large Language Models (LLMs) can correct their own errors during operation, known as 'self-correction.' It organizes various approaches, from using internal mechanisms to integrating external tools or information, helping you understand the latest research. This resource is for AI researchers, machine learning engineers, and data scientists working on improving LLM reliability and performance.
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Use this if you are researching methods to make LLMs more accurate and reliable by enabling them to refine their own responses.
Not ideal if you are looking for ready-to-use software or code examples for deploying self-correcting LLMs in a production environment.
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Dec 28, 2024
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