Model Internals-based Answer Attribution for Trustworthy
  Retrieval-Augmented Generation
Authors: name: Jirui Qi, name: Gabriele Sarti, name: Raquel Fernández, name: Arianna Bisazza

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.