Conformal Factuality Control for Multi-Hop Retrieval-Augmented Generation

Abstract:Retrieval-augmented generation (RAG) can ground large language models in external evidence, but retrieved context does not guarantee that generated claims are factually supported. This problem is especially relevant in multi-hop RAG, where retrieval and reasoning proceed through multiple dependent stages. We study whether claim-level conformal factuality control, previously developed for RAG, remains effective in this setting. We apply split-conformal claim filtering to multi-hop RAG and evaluate it on HotpotQA, Natural Questions, and TriviaQA using Llama 3.1 8B and GPT-4o-mini, together with a single-hop reference experiment. Across all six multi-hop model-dataset configurations, increasingly stringent conformal targets consistently increase the fraction of responses whose retained claims are fully supported. At the 95% target, this rate ranges from 95.80% to 97.20%, compared with 55.60%-76.03% without filtering. However, the improvement is strongly selective: only 4.41%-31.09% of generated claims are retained and 9.70%-51.40% of responses remain non-empty at the 95% target. These results show that conformal factuality extends to multi-hop RAG, while demonstrating that nominal reliability must be interpreted jointly with claim retention and abstention.

15 pages, 2 figures. Code available at this https URL

Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2609.38222 [cs.CL]

(or arXiv:2609.38222v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.38222 Focus to learn more arXiv-issued DOI via DataCite

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