论文库

原文摘要

Retrieval-augmented generation (RAG) improves the grounding of language-model responses through external evidence, yet retrieved information alone does not ensure factual support for generated claims. In agentic RAG, evidence acquisition is further complicated by adaptive retrieval decisions, where an agent iteratively determines whether to retrieve additional information or terminate the search. We investigate how conformal factuality can be applied in this setting to improve the reliability of generated responses. We formalize the agent-driven retrieval process and characterize a query-aware relevance-scoring formulation that accounts for evidence collected across successive retrieval decisions. Building on this formulation, we evaluate post-generation conformal claim filtering using GPT-6 Luna and Llama 3.1 8B across HotpotQA, Natural Questions, and TriviaQA. Our experiments demonstrate that increasingly stringent conformal targets are consistently associated with higher observed response-level factual support, accompanied by reductions in claim retention and non-empty response coverage. The extent of these trade-offs varies across models and datasets, highlighting the importance of jointly evaluating factual reliability and answer availability. These findings establish an empirical foundation for studying conformal factuality in agent-driven retrieval systems and motivate further investigation into improving the reliability of their generated responses.