Citation Failure: Definition, Analysis and Efficient Mitigation

arXiv cs.CL / 4/29/2026

💬 OpinionModels & Research

Key Points

  • Citation failure in LLM-based RAG systems occurs when models produce helpful answers but fail to attach complete, verifiable citations, weakening response verification.
  • The paper distinguishes citation failure from response failure, where the answer itself is incorrect or evidence cannot be cited, and studies how the relationship between response and evidence impacts citation quality.
  • It introduces CITECONTROL, a benchmark that systematically varies the response–evidence relation to analyze failure modes, showing that failures grow with increasing relational complexity.
  • To mitigate citation failure efficiently, it proposes CITENTION, a framework combining generative, attention-based, and retrieval-based citation strategies, which yields substantial citation improvements on CITECONTROL and in transfer settings.
  • The authors release the associated data and code to support further research and replication.

Abstract

Citations from LLM-based RAG systems are supposed to simplify response verification. However, this goal is undermined in cases of citation failure, where a model generates a helpful response, but fails to generate citations to complete evidence. In contrast to previous work, we propose to disentangle this from response failure, where the response itself is flawed, and citing complete evidence is impossible. To address citation failure, this work follows a two-step approach: (1) We study when citation failure occurs and (2) how it can be mitigated efficiently. For step 1, we extend prior work by investigating how the relation between response and evidence affects citation quality. We introduce CITECONTROL, a benchmark that systematically varies this relation to enable the analysis of failure modes. Experiments show that failures increase with relational complexity and suggest that combining citation methods could improve performance, motivating step 2. To study the efficient improvement of LLM citation, we propose CITENTION, a framework integrating generative, attention-based, and retrieval-based methods. Results demonstrate substantial citation improvements on CITECONTROL and in transfer settings. We make our data and code publicly available.