Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation

arXiv:2404.06809v1 Announce Type: new
Abstract: The rapid development of large language models has led to the widespread adoption of Retrieval-Augmented Generation (RAG), which integrates external knowledge to alleviate knowledge bottlenecks and mitigate hallucinations. However, the existing RAG paradigm inevitably suffers from the impact of flawed information introduced during the retrieval phrase, thereby diminishing the reliability and correctness of the generated outcomes. In this paper, we propose Credibility-aware Generation (CAG), a universally applicable framework designed to mitigate the impact of flawed …

abstract adoption arxiv bottlenecks cs.cl development hallucinations however impact information knowledge language language models large language large language models llms paradigm rag retrieval retrieval-augmented teaching type