IAAR-Shanghai/ICSFSurvey
Explore concepts like Self-Correct, Self-Refine, Self-Improve, Self-Contradict, Self-Play, and Self-Knowledge, alongside o1-like reasoning elevation🍓 and hallucination alleviation🍄.
This project is a comprehensive survey that explores how Large Language Models (LLMs) can evaluate and improve their own performance. It helps AI researchers, machine learning engineers, and data scientists understand methods to reduce incorrect outputs (hallucinations) and enhance reasoning abilities in LLMs. The project provides a curated list of research papers and experimental results demonstrating consistency measurement and response analysis.
172 stars. No commits in the last 6 months.
Use this if you are an AI researcher or practitioner looking to understand the current landscape of self-correction, self-refinement, and hallucination alleviation techniques in large language models.
Not ideal if you are looking for an out-of-the-box software tool to directly apply to your LLM projects without diving into research papers.
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Jupyter Notebook
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Last pushed
Dec 07, 2024
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