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Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review

Author

Listed:
  • Wan Zhang

    (School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China)

  • Jing Zhang

    (School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China)

Abstract

Retrieval-augmented generation (RAG) leverages the strengths of information retrieval and generative models to enhance the handling of real-time and domain-specific knowledge. Despite its advantages, limitations within RAG components may cause hallucinations, or more precisely termed confabulations in generated outputs, driving extensive research to address these limitations and mitigate hallucinations. This review focuses on hallucination in retrieval-augmented large language models (LLMs). We first examine the causes of hallucinations from different sub-tasks in the retrieval and generation phases. Then, we provide a comprehensive overview of corresponding hallucination mitigation techniques, offering a targeted and complete framework for addressing hallucinations in retrieval-augmented LLMs. We also investigate methods to reduce the impact of hallucination through detection and correction. Finally, we discuss promising future research directions for mitigating hallucinations in retrieval-augmented LLMs.

Suggested Citation

  • Wan Zhang & Jing Zhang, 2025. "Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review," Mathematics, MDPI, vol. 13(5), pages 1-33, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:856-:d:1605417
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    References listed on IDEAS

    as
    1. Murray Shanahan & Kyle McDonell & Laria Reynolds, 2023. "Role play with large language models," Nature, Nature, vol. 623(7987), pages 493-498, November.
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