Published on July 30, 2024, the summary describes a framework that evaluates retrieved documents, selects evidence, and examines reasoning steps before answering.
Published on July 30, 2024, the item describes a self-reasoning framework for retrieval-augmented generation (RAG). It uses a language model to assess the relevance of retrieved documents, select and cite passages as evidence, and analyze those trajectories before producing an answer.
According to the post, the framework achieves performance comparable to GPT-4 with 2,000 training examples generated by that model. Relevance assessment and evidence selection are presented as ways to make RAG answers more traceable. To check the result and its limits, consult the original post, review its method description, and examine the evaluation and conditions reported by the source.