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

Understand

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.