Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2406.13663v4
• Date: Fri, 18 Oct 2024 13:16:57 GMT
• Title: Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
• Authors: Jirui Qi, Gabriele Sarti, Raquel Fernández, Arianna Bisazza,
• Abstract summary: We present MIRAGE --Model Internals-based RAG Explanations -- a plug-and-play approach using model internals for faithful answer attribution in question answering applications. We evaluate our proposed approach on a multilingual QA dataset, finding high agreement with human answer attribution.
• Score: 8.975024781390077
• License:
• Abstract: 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.

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