Self-RAG: A Self-Reflective Framework for Improving LLM Factuality and Output Quality
By
Akari Asai1
Summary
Self-RAG is a framework that enhances large language models by training them to retrieve relevant information, generate responses, and critique their own outputs through self-reflection. It addresses the problem of factual inaccuracies and hallucinations in LLMs by incorporating on-demand retrieval and self-critique mechanisms. The approach outperforms ChatGPT and retrieval-augmented LLama2 Chat across six tasks by improving factuality and output quality without requiring additional training data.
Source
Twitter / XSelf-RAG: A Self-Reflective Framework for Improving LLM Factuality and Output Qualityselfrag.github.ioKey quotes
· 3 pulledSelf-RAG learns to retrieve, generate and critique to enhance LM's output quality and factuality, outperforming ChatGPT and retrieval-augmented LLama2 Chat on six tasks.
Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate.
They often generate hallucinations, especially in long-tail, their knowledge gets obsolete.
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