---
title: "Who cites Jaehong Yoon — 215 papers of our library"
description: "215 papers of the Elamur Research library cite the works of Jaehong Yoon, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jaehong-yoon/cited-by/"
---
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# Who cites Jaehong Yoon

232 citations of their works from 215 papers of our library (of the 149,439 whose bibliographies we read) · 45 with a page here, the most cited first · [← Jaehong Yoon](https://md.research.elamur.ai/free/en/authors/jaehong-yoon/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
3. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
4. **[OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://md.research.elamur.ai/free/en/papers/huang-2023-opera-alleviating-hallucination-multi-modal/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Qidong Huang, Xiaoyi Dong, Pan Zhang, et al.*2023 · 238 citations*
5. **[When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 161 citations*
6. [**Hallucination of Multimodal Large Language Models: A Survey arXiv:2404.18930**Zechen Bai, Pichao Wang, Tianjun Xiao, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-hallucination-multimodal-large-language-models/)
7. [**VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs arXiv:2406.07476**Zesen Cheng, Sicong Leng, Hang Zhang, et al.*2024 · 118 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2024-videollama-2-advancing-spatial-temporal/)
8. [**A Survey on Hallucination in Large Vision-Language Models arXiv:2402.00253**Hanchao Liu, Wenyuan Xue, Yifei Chen, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-survey-hallucination-large-vision-language/)
9. **[Factuality of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.*2024 · 77 citations*
10. **[MANTIS: Interleaved Multi-Image Instruction Tuning](https://md.research.elamur.ai/free/en/papers/jiang-2024-mantis-interleaved-multi-image-instruction/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Dongfu Jiang, Xuan He, Huaye Zeng, et al.*2024 · 74 citations*
11. [**AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation arXiv:2311.07397**Junyang Wang, Yuhang Wang, Guohai Xu, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-amber-llm-free-multi-dimensional/)
12. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
13. [**MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark arXiv:2402.04788**Dongping Chen, Ruoxi Chen, Shilin Zhang, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-mllm-judge-assessing-multimodal-llm/)
14. **[Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence](https://md.research.elamur.ai/free/en/papers/he-2025-cracking-code-hallucination-lvlms-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinghan He, Kuan Zhu, Haiyun Guo, et al.*2025 · 21 citations*
15. **[Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs](https://md.research.elamur.ai/free/en/papers/zhang-2025-shallow-focus-deep-fixes-enhancing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaofeng Zhang, Yihao Quan, Chen Shen, et al.*2025 · 14 citations*
16. **[Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA](https://md.research.elamur.ai/free/en/papers/park-2026-too-many-frames-not-all/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jongwoo Park, Kanchana Ranasinghe, Kumara Kahatapitiya, et al.*2026 · 13 citations*
17. **[Activation Steering Decoding: Mitigating Hallucination in Large Vision-Language Models through Bidirectional Hidden State Intervention](https://md.research.elamur.ai/free/en/papers/su-2025-activation-steering-decoding-mitigating-hallucination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingran Su, Jingfan Chen, Hongxin Li, et al.*2025 · 12 citations*
18. **[Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models](https://md.research.elamur.ai/free/en/papers/lei-2025-scaffolding-coordinates-promote-vision-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xuanyu Lei, Zonghan Yang, Xinrui Chen, et al.*2025 · 11 citations*
19. **[HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding](https://md.research.elamur.ai/free/en/papers/zhang-2026-hermes-kv-cache-as-hierarchical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haowei Zhang, Shudong Yang, Jinlan Fu, et al.*2026 · 9 citations*
20. **[VideoPro: Adaptive Program Reasoning for Long Video Understanding](https://md.research.elamur.ai/free/en/papers/li-2026-videopro-adaptive-program-reasoning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenglin Li, Feng Han, Yikun Wang, et al.*2026 · 6 citations*
21. **[Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs](https://md.research.elamur.ai/free/en/papers/lyu-2026-revealing-enhancing-core-visual-regions/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guangtao Lyu, Qi Liu, Chenghao Xu, et al.*2026 · 5 citations*
22. **[RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback](https://md.research.elamur.ai/free/en/papers/chen-2025-rrhf-v-ranking-responses-mitigate/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Guoqing Chen, Fu Zhang, Jinghao Lin, et al.*2025 · 5 citations*
23. **[Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding](https://md.research.elamur.ai/free/en/papers/mao-2026-through-magnifying-glass-adaptive-perception/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shunqi Mao, Chaoyi Zhang, Weidong Cai*2026 · 4 citations*
24. **[LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://md.research.elamur.ai/free/en/papers/liu-2026-longvideoagent-multi-agent-reasoning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Runtao Liu, Ziyi Liu, Jiaqi Tang, et al.*2026 · 3 citations*
25. **[VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck](https://md.research.elamur.ai/free/en/papers/zhang-2026-vib-probe-detecting-mitigating-hallucinations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Feiran Zhang, Yixin Wu, Zhenghua Wang, et al.*2026 · 3 citations*
26. **[Agentic Very Long Video Understanding](https://md.research.elamur.ai/free/en/papers/rege-2026-agentic-very-long-video-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aniket Rege, Arka Sadhu, Yuliang Li, et al.*2026 · 1 citation*
27. **[DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning](https://md.research.elamur.ai/free/en/papers/sivakumaran-2026-dart-leveraging-multi-agent-disagreement/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Nithin Sivakumaran, Justin Chen, David Wan, et al.*2026 · 1 citation*
28. **[Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering](https://md.research.elamur.ai/free/en/papers/liu-2026-vision-language-introspection-mitigating-overconfident/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuliang Liu, Songbo Yang, Dong Fang, et al.*2026 · 1 citation*
29. **[CEBC: Conformal Evidence-Bounded Control for Low-Hallucination Vision–Language Generation](https://md.research.elamur.ai/free/en/papers/mishra-2026-cebc-conformal-evidence-bounded-control/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ashish Mishra, Tarun Kumar, Arpit Shah, et al.*2026*
30. **[Coarse-to-Fine Multimodal Information Selection for Video Speaking Style Recognition with Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-coarse-fine-multimodal-information-selection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beibei Zhang, Yanan Lu, Lin Fen, et al.*2026*
31. **[DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/yang-2026-dica-dual-indicator-guided-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Yang, Jin Wang, Xuejie Zhang*2026*
32. **[Faithful Medical Dialogue Generation Using Homo-Heterogeneous Exemplar-based In-Context Knowledge Grounding](https://md.research.elamur.ai/free/en/papers/priya-2026-faithful-medical-dialogue-generation-homo/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Priyanshu Priya, Hardik Goyal, Asif Ekbal*2026*
33. **[Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking](https://md.research.elamur.ai/free/en/papers/uppaal-2026-journey-before-destination-importance-visual/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rheeya Uppaal, Phu Mon Htut, Min Bai, et al.*2026*
34. **[Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations](https://md.research.elamur.ai/free/en/papers/zhao-2026-learning-flexible-large-multimodal-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyu Zhao, Kangqi Ni, Jie Peng, et al.*2026*
35. **[Minimal Clips, Maximum Salience: Long Video Summarization via Key Moment Extraction](https://md.research.elamur.ai/free/en/papers/pennec-2026-minimal-clips-maximum-salience-long/) [International Workshop on Spoken Dialogue System Technology](https://md.research.elamur.ai/free/en/venues/iwsds/)**Galann Pennec, Zhengyuan Liu, Nicholas Asher, et al.*2026*
36. **[Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement](https://md.research.elamur.ai/free/en/papers/qin-2026-mitigating-action-relation-hallucinations-lvlms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhenxin Qin, Qiang Li, Qingzhuo Wang, et al.*2026*
37. **[Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation](https://md.research.elamur.ai/free/en/papers/zhu-2026-mitigating-hallucinations-large-vision-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingyu Zhu, Junfeng Fang, Shuo Wang, et al.*2026*
38. **[Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://md.research.elamur.ai/free/en/papers/sarwar-2026-multimodal-unlearning-across-vision-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, et al.*2026*
39. **[Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods](https://md.research.elamur.ai/free/en/papers/yuan-2026-reducing-token-redundancy-lvlms-systematic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanzhang Yuan, Mengxuan Hu, Wenhao Zhang, et al.*2026*
40. **[Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection](https://md.research.elamur.ai/free/en/papers/niu-2026-routing-generated-data-annotation-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyi Niu, Justin Chen, Genta Indra Winata, et al.*2026*
41. **[SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series](https://md.research.elamur.ai/free/en/papers/pennec-2026-sagaqa-multi-hop-reasoning-benchmark/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Galann Pennec, Zhengyuan Liu, Nicholas Asher, et al.*2026*
42. **[SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning](https://md.research.elamur.ai/free/en/papers/yang-2026-scope-preserving-modality-specific-cues/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jingfan Yang, Rui Zhang, Liang Hong, et al.*2026*
43. **[Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning](https://md.research.elamur.ai/free/en/papers/wan-2026-test-time-scaling-multimodal-foundation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cong Wan, Ying He, Zhongzhan Huang, et al.*2026*
44. **[TokenPenalty: Alleviating Attention Sinks and Positional Decay in LVLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-tokenpenalty-alleviating-attention-sinks-positional/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaofeng Zhang, Yuanchao Zhu, Qiyan Zhao, et al.*2026*
45. **[VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation](https://md.research.elamur.ai/free/en/papers/park-2026-vauq-vision-aware-uncertainty-quantification/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seongheon Park, Changdae Oh, Hyeong Kyu Choi, et al.*2026*

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