🔗 Who cites «Woodpecker: Hallucination Correction for Multimodal Large Language Models»

88 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · ← back to «Woodpecker: Hallucination Correction for Multimodal Large Language Models»

1. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsACM Transactions on Information SystemsLei Huang, Weijiang Yu, Weitao Ma, et al.2023 · 3,231 citations
2. MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Chaoyou Fu, Peixian Chen, Yunhang Shen, et al.2023 · 554 citations
3. HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language ModelsIEEE Computer Society Conference on Computer Vision and Pattern RecognitionTianrui Guan, Fuxiao Liu, Xiyang Wu, et al.2023 · 335 citations
4. OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-AllocationIEEE Computer Society Conference on Computer Vision and Pattern RecognitionQidong Huang, Xiaoyi Dong, Pan Zhang, et al.2023 · 238 citations
5. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
6. RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human FeedbackIEEE Computer Society Conference on Computer Vision and Pattern RecognitionTianyu Yu, Yuan Yao, Haoye Zhang, et al.2023 · 217 citations
7. When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsTransactions of the Association for Computational Linguistics (TACL)Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.2024 · 161 citations
8. Hallucination of Multimodal Large Language Models: A SurveyarXiv:2404.18930Zechen Bai, Pichao Wang, Tianjun Xiao, et al.2024 · 120 citations
9. A Survey on Hallucination in Large Vision-Language ModelsarXiv:2402.00253Hanchao Liu, Wenyuan Xue, Yifei Chen, et al.2024 · 113 citations
10. InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large ModelarXiv:2401.16420Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.2024 · 94 citations
11. Factuality of Large Language Models: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.2024 · 77 citations
12. AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination EvaluationarXiv:2311.07397Junyang Wang, Yuhang Wang, Guohai Xu, et al.2023 · 67 citations
13. MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language BenchmarkarXiv:2402.04788Dongping Chen, Ruoxi Chen, Shilin Zhang, et al.2024 · 60 citations
14. Cracking the Code of Hallucination in LVLMs with Vision-aware Head DivergenceAnnual Meeting of the Association for Computational Linguistics (ACL)Jinghan He, Kuan Zhu, Haiyun Guo, et al.2025 · 21 citations
15. Activation Steering Decoding: Mitigating Hallucination in Large Vision-Language Models through Bidirectional Hidden State InterventionAnnual Meeting of the Association for Computational Linguistics (ACL)Jingran Su, Jingfan Chen, Hongxin Li, et al.2025 · 12 citations
16. Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMsFindings of the Association for Computational LinguisticsGuangtao Lyu, Qi Liu, Chenghao Xu, et al.2026 · 5 citations
17. RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human FeedbackInternational Conference on Computational Linguistics (COLING)Guoqing Chen, Fu Zhang, Jinghao Lin, et al.2025 · 5 citations
18. Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM DecodingAnnual Meeting of the Association for Computational Linguistics (ACL)Shunqi Mao, Chaoyi Zhang, Weidong Cai2026 · 4 citations
19. VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information BottleneckAnnual Meeting of the Association for Computational Linguistics (ACL)Feiran Zhang, Yixin Wu, Zhenghua Wang, et al.2026 · 3 citations
20. Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal SteeringAnnual Meeting of the Association for Computational Linguistics (ACL)Shuliang Liu, Songbo Yang, Dong Fang, et al.2026 · 1 citation
21. Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Sirat Samyoun, Yingtai Xiao, Jian Du2026
22. Journey Before Destination: On the importance of Visual Faithfulness in Slow ThinkingConference of the European Chapter of the Association for Computational Linguistics (EACL)Rheeya Uppaal, Phu Mon Htut, Min Bai, et al.2026
23. Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiwen Luo, Siyu Jiang, Weilong Jiang, et al.2026
24. Mitigating Hallucinations in Large Vision-Language Models without Performance DegradationAnnual Meeting of the Association for Computational Linguistics (ACL)Xingyu Zhu, Junfeng Fang, Shuo Wang, et al.2026

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