Correctness is not Faithfulness in Retrieval Augmented Generation Attributions
Jonas Wallat, Maria Heuss, Maarten de Rijke, Avishek Anand · 2025
Large language models (LLMs) have transformed information retrieval through chat interfaces, but their hallucination tendencies pose significant risks. While Retrieval Augmented Generation (RAG) with citations has emerged as a solution by allowing users to verify responses through source attribution, current evaluation approaches focus primarily on citation correctness - whether cited documents support the corresponding statements. This is insufficient and we introduce citation faithfulness - whether the model's reliance on cited documents is genuine rather than post-rationalized to fit pre-existing knowledge. Our contributions are threefold: (i) we introduce coherent notions of attribution and introduce the concept of citation faithfulness; (ii) we propose desiderata for citations beyond correctness and accuracy needed for trustworthy systems; and (iii) we emphasize evaluating citation faithfulness by studying post-rationalization. Through experimentation, we reveal prevalent post-rationalization issues, finding that up to 57% of citations lack faithfulness. This undermines reliable attribution and may result in misplaced trust, highlighting a critical gap in current LLM-based IR systems. We demonstrate why both citation correctness and faithfulness must be considered when deploying LLMs in IR applications, contributing to a broader discussion of building more reliable and transparent information access systems.