title: Model Internals-based Answer Attribution for Trustworthy
    Retrieval-Augmented Generation
summary: 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.
paper_authors._id: 667586138794bbd33fde01f3
paper_authors.name: Jirui Qi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-21T15:00:34.930Z
paper_authors._id: 645a415077d040cfd64e036e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/645a415077d040cfd64e036e/2k6QsstgWC1H4oRMlzcc5.png
paper_authors.fullname: Jirui Qi
paper_authors.type: user
paper_authors.user: JRQi
paper_authors._id: 667586138794bbd33fde01f4
paper_authors.name: Gabriele Sarti
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-21T14:20:43.979Z
paper_authors._id: 5e7749883d77a72421292d07
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5e7749883d77a72421292d07/M4AmBReZk_otxCIG3o0bL.jpeg
paper_authors.fullname: Gabriele Sarti
paper_authors.type: user
paper_authors.user: gsarti
paper_authors._id: 667586138794bbd33fde01f5
paper_authors.name: Raquel Fernández
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-07T16:42:58.263Z
paper_authors._id: 66055fcc196d49210e4f8ad9
paper_authors.avatarUrl: /avatars/a6d3d10929fcf99e58391a55b5250bd4.svg
paper_authors.fullname: Raquel Fernández
paper_authors.type: user
paper_authors.user: rfernandez
paper_authors._id: 667586138794bbd33fde01f6
paper_authors.name: Arianna Bisazza
paper_title: Model Internals-based Answer Attribution for Trustworthy
    Retrieval-Augmented Generation
paper_summary: 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.
paper_ai_summary: MIRAGE, a plug-and-play method using model internals, enhances retrieval-augmented generation by accurately attributing answer tokens to relevant documents, achieving high agreement with human attributions and competitive citation quality.
paper_ai_keywords: retrieval-augmented generation RAG question answering QA self-citation prompting large language models LLMs MIRAGE model internals extractive QA open-ended QA saliency methods answer attribution