Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
Jirui Qi, Gabriele Sarti, Raquel Fernández, Arianna Bisazza · 2024
Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain.Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers.However, self-citing LLMs often struggle to match the required format, refer to non-existent sources, and fail to faithfully reflect LLMs' context usage throughout the generation.In this work, we present MIRAGE -Model Internals-based RAG Explanations -a plug-and-play approach using model internals for faithful answer attribution in RAG applications.MIRAGE detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction via saliency methods.We evaluate our proposed approach on a multilingual extractive QA dataset, finding high agreement with human answer attribution.On open-ended QA, MIRAGE achieves citation quality and efficiency comparable to self-citation while also allowing for a finer-grained control of attribution parameters.Our qualitative evaluation highlights the faithfulness of MIRAGE's attributions and underscores the promising application of model internals for RAG answer attribution. 1