Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review
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...
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/5/856 |
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| author | Wan Zhang Jing Zhang |
| author_facet | Wan Zhang Jing Zhang |
| author_sort | Wan Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8ba9ce8fab324025aefc99bc78106712 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-8ba9ce8fab324025aefc99bc781067122025-08-20T02:05:24ZengMDPI AGMathematics2227-73902025-03-0113585610.3390/math13050856Hallucination Mitigation for Retrieval-Augmented Large Language Models: A ReviewWan Zhang0Jing Zhang1School of Cyber Science and Engineering, Southeast University, Nanjing 211189, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing 211189, ChinaRetrieval-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.https://www.mdpi.com/2227-7390/13/5/856large language modelshallucinationretrieval-augmented generationhallucination mitigation |
| spellingShingle | Wan Zhang Jing Zhang Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review Mathematics large language models hallucination retrieval-augmented generation hallucination mitigation |
| title | Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review |
| title_full | Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review |
| title_fullStr | Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review |
| title_full_unstemmed | Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review |
| title_short | Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review |
| title_sort | hallucination mitigation for retrieval augmented large language models a review |
| topic | large language models hallucination retrieval-augmented generation hallucination mitigation |
| url | https://www.mdpi.com/2227-7390/13/5/856 |
| work_keys_str_mv | AT wanzhang hallucinationmitigationforretrievalaugmentedlargelanguagemodelsareview AT jingzhang hallucinationmitigationforretrievalaugmentedlargelanguagemodelsareview |