---
title: "Who cites Dinesh Manocha — 294 papers of our library"
description: "294 papers of the Elamur Research library cite the works of Dinesh Manocha, found in their bibliographies; the most cited first."
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---
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# Who cites Dinesh Manocha

317 citations of their works from 294 papers of our library (of the 149,439 whose bibliographies we read) · 100 with a page here, the most cited first · [← Dinesh Manocha](https://md.research.elamur.ai/free/en/authors/dinesh-manocha/)

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. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
4. **[MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/) [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/)**Xiang Yue, Yuansheng Ni, Kai Zhang, et al.*2023 · 1,005 citations*
5. [**Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution arXiv:2409.12191**Peng Wang, Shuai Bai, Sinan Tan, et al.*2024 · 860 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/)
6. **[How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhe Chen, Weiyun Wang, Hao Tian, et al.*2024 · 541 citations*
7. [**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/)
8. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
9. [**Qwen2.5-Omni Technical Report arXiv:2503.20215**Jin Xu, Zhifang Guo, Jinzheng He, et al.*2025 · 227 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen2-5-omni-technical/)
10. **[RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://md.research.elamur.ai/free/en/papers/yu-2023-rlhf-v-towards-trustworthy-mllms/) [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/)**Tianyu Yu, Yuan Yao, Haoye Zhang, et al.*2023 · 217 citations*
11. [**InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models arXiv:2504.10479**Jinguo Zhu, Weiyun Wang, Zhe Chen, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)
12. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
13. [**Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning arXiv:2306.14565**Fuxiao Liu, Kevin Lin, Linjie Li, et al.*2023 · 189 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/)
14. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency arXiv:2508.18265**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/)
15. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs arXiv:2503.01743**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 152 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
16. **[LLaVA-CoT: Let Vision Language Models Reason Step-by-Step](https://md.research.elamur.ai/free/en/papers/xu-2024-llava-cot-let-vision-language/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Guowei Xu, Peng Jin, Ziang Wu, et al.*2024 · 138 citations*
17. [**MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark arXiv:2409.02813**Xiang Yue, Tianyu Zheng, Yuansheng Ni, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mmmu-pro-more-robust-multi/)
18. [**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/)
19. [**Qwen3-Omni Technical Report arXiv:2509.17765**Jin Xu, Zhifang Guo, Hangrui Hu, et al.*2025 · 117 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen3-omni-technical-report/)
20. **[LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-lmms-eval-reality-check-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaichen Zhang, Bo Li, Peiyuan Zhang, et al.*2025 · 115 citations*
21. **[A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://md.research.elamur.ai/free/en/papers/laskar-2024-systematic-survey-critical-review-evaluating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.*2024 · 105 citations*
22. [**ChartLlama: A Multimodal LLM for Chart Understanding and Generation arXiv:2311.16483**Yucheng Han, Chi Zhang, Xin Chen, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-chartllama-multimodal-llm-chart-understanding/)
23. [**Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models arXiv:2503.06749**Wenxuan Huang, Bohan Jia, Zijie Zhai, et al.*2025 · 92 citations*](https://md.research.elamur.ai/free/en/papers/huang-2025-vision-r1-incentivizing-reasoning-capability/)
24. [**GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning arXiv:2507.01006**GLM-V Team, Wenyi Hong, Wenmeng Yu, et al.*2025 · 89 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-v-glm-4-5v-glm/)
25. **[MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2024-mmc-advancing-multimodal-chart-understanding/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, et al.*2024 · 86 citations*
26. **[Recent Advances in Speech Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/cui-2025-recent-advances-speech-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao, et al.*2025 · 69 citations*
27. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/)
28. [**MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark arXiv:2410.19168**S Sakshi, Utkarsh Tyagi, Sonal Kumar, et al.*2024 · 67 citations*](https://md.research.elamur.ai/free/en/papers/sakshi-2024-mmau-massive-multi-task-audio/)
29. [**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/)
30. [**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/)
31. [**Kimi-Audio Technical Report arXiv:2504.18425**KimiTeam, Ding Ding, Zeqian Ju, et al.*2025 · 57 citations*](https://md.research.elamur.ai/free/en/papers/kimiteam-2025-kimi-audio-technical-report/)
32. [**Step-Audio 2 Technical Report arXiv:2507.16632**Boyong Wu, Chao Yan, Chen Hu, et al.*2025 · 31 citations*](https://md.research.elamur.ai/free/en/papers/wu-2025-step-audio-2-technical/)
33. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/)
34. [**Qwen3.5-Omni Technical Report arXiv:2604.15804**Qwen Team*2026 · 29 citations*](https://md.research.elamur.ai/free/en/papers/qwen-team-2026-qwen3-5-omni-technical/)
35. [**Baichuan-Omni-1.5 Technical Report arXiv:2501.15368**Yadong Li, Jun Liu, Tao Zhang, et al.*2025 · 28 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-baichuan-omni-1-5/)
36. **[Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://md.research.elamur.ai/free/en/papers/zhifei-2025-audio-reasoner-improving-reasoning-capability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xie Zhifei, Mingbao Lin, Zihang Liu, et al.*2025 · 25 citations*
37. **[LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering](https://md.research.elamur.ai/free/en/papers/bi-2025-llava-steering-visual-instruction-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinhe Bi, Yujun Wang, Haokun Chen, et al.*2025 · 24 citations*
38. [**MiMo-Audio: Audio Language Models are Few-Shot Learners arXiv:2512.23808**Xiaomi LLM-Core Team, Dong Zhang, Gang Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-llm-mimo-audio-audio-language/)
39. [**MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe arXiv:2509.18154**Tianyu Yu, Zefan Wang, Chongyi Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-minicpm-v-4-5/)
40. **[Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench](https://md.research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zheyuan Liu, Guangyao Dou, Mengzhao Jia, et al.*2025 · 24 citations*
41. **[Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-multimodal-needle-haystack-benchmarking-long/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hengyi Wang, Haizhou Shi, Shiwei Tan, et al.*2025 · 21 citations*
42. [**Baichuan-Omni Technical Report arXiv:2410.08565**Yadong Li, Haoze Sun, Mingan Lin, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-baichuan-omni-technical-report/)
43. **[Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/yang-2025-holistic-evaluation-large-audio-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chih-Kai Yang, Neo S. Ho, Hung-yi Lee*2025 · 17 citations*
44. **[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*
45. **[A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/anikina-2025-rigorous-evaluation-llm-data-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tatiana Anikina, Jan Cegin, Jakub Simko, et al.*2025 · 11 citations*
46. **[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*
47. [**Implicit Reasoning in Large Language Models: A Comprehensive Survey arXiv:2509.02350**Jindong Li, Yali Fu, Li Fan, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-implicit-reasoning-large-language-models/)
48. **[WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning](https://md.research.elamur.ai/free/en/papers/mundada-2025-wildscore-benchmarking-mllms-wild-symbolic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gagan Mundada, Yash Vishe, Amit Namburi, et al.*2025 · 10 citations*
49. **[VoxEval: Benchmarking the Knowledge Understanding Capabilities of End-to-End Spoken Language Models](https://md.research.elamur.ai/free/en/papers/cui-2025-voxeval-benchmarking-knowledge-understanding-capabilities/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqian Cui, Xiaoqi Jiao, Ziqiao Meng, et al.*2025 · 9 citations*
50. **[Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better](https://md.research.elamur.ai/free/en/papers/wang-2026-autoregressive-semantic-visual-reconstruction-helps/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dianyi Wang, Wei Song, Yikun Wang, et al.*2026 · 7 citations*
51. [**ERNIE 5.0 Technical Report arXiv:2602.04705**Haifeng Wang, Hua Wu, Tian Wu, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2026-ernie-5-0-technical/)
52. **[Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-insight-over-sight-exploring-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Liu, Wenxuan Wang, Youliang Yuan, et al.*2025 · 6 citations*
53. [**Skywork-R1V3 Technical Report arXiv:2507.06167**Wei Shen, Jiangbo Pei, Yi Peng, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shen-2025-skywork-r1v3-technical-report/)
54. [**Thinking with Sound: Audio Chain-of-Thought Enables Multimodal Reasoning in Large Audio-Language Models arXiv:2509.21749**Zhen Xiong, Yujun Cai, Zhecheng Li, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2025-thinking-sound-audio-chain-thought/)
55. [**MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction arXiv:2604.27393**Junbo Cui, Bokai Xu, Chongyi Wang, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/cui-2026-minicpm-o-4-5/)
56. **[OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning](https://md.research.elamur.ai/free/en/papers/lu-2026-octotools-multi-agent-framework-extensible/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pan Lu, Bowen Chen, Sheng Liu, et al.*2026 · 5 citations*
57. **[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*
58. **[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*
59. **[Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://md.research.elamur.ai/free/en/papers/oh-2026-uncertainty-quantification-llm-agents-foundations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Changdae Oh, Seongheon Park, To Eun Kim, et al.*2026 · 4 citations*
60. **[Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models](https://md.research.elamur.ai/free/en/papers/song-2026-audio-jailbreak-open-comprehensive-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zirui Song, Qian Jiang, Mingxuan Cui, et al.*2026 · 3 citations*
61. **[AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation](https://md.research.elamur.ai/free/en/papers/manakul-2026-audiojudge-understanding-what-works-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Potsawee Manakul, Woody Haosheng Gan, Michael J Ryan, et al.*2026 · 3 citations*
62. [**Step-Audio-R1 Technical Report arXiv:2511.15848**Fei Tian, Xiangyu Tony Zhang, Yuxin Zhang, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/tian-2025-step-audio-r1-technical/)
63. **[Attacker's Noise Can Manipulate Your Audio-based LLM in the Real World](https://md.research.elamur.ai/free/en/papers/sadasivan-2026-attacker-s-noise-can-manipulate/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Vinu Sankar Sadasivan, Soheil Feizi, Rajiv Mathews, et al.*2026 · 2 citations*
64. **[Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-do-images-speak-louder-than/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chi Zhang, Wenxuan Ding, Jiale Liu, et al.*2026 · 2 citations*
65. [**STEP3-VL-10B Technical Report arXiv:2601.09668**Ailin Huang, Chengyuan Yao, Chunrui Han, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/huang-2026-step3-vl-10b-technical/)
66. **[When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/ortu-2026-when-seeing-overrides-knowing-disentangling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Francesco Ortu, Zhijing Jin, Diego Doimo, et al.*2026 · 2 citations*
67. [**Benchmarking Vision Language Models on German Factual Data arXiv:2504.11108**Rene Peinl, Vincent Tischler*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/peinl-2025-benchmarking-vision-language-models-german/)
68. **[Octopus: Towards Building the Arabic Speech LLM Suite](https://md.research.elamur.ai/free/en/papers/althubaiti-2025-octopus-building-arabic-speech-llm/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sara Althubaiti, Vasista Sai Lodagala, Tjad Clark, et al.*2025 · 2 citations*
69. **[HumMusQA: A Human-written Music Understanding QA Benchmark Dataset](https://md.research.elamur.ai/free/en/papers/weck-2026-hummusqa-human-written-music-understanding/) [Workshop on NLP for Music and Audio](https://md.research.elamur.ai/free/en/venues/nlp4musa/)**Benno Weck, Pablo Puentes, Andrea Poltronieri, et al.*2026 · 1 citation*
70. **[Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding](https://md.research.elamur.ai/free/en/papers/wang-2026-listen-pause-reason-perception-grounded/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jieyi Wang, Yazhe Niu, Dexuan Xu, et al.*2026 · 1 citation*
71. **[MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-mmerror-benchmark-erroneous-reasoning-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Shi, Yifeng Xie, Minzhe Guo, et al.*2026 · 1 citation*
72. **[Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation](https://md.research.elamur.ai/free/en/papers/tseng-2026-revisiting-audio-language-pretraining-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, et al.*2026 · 1 citation*
73. **[Rubric-Guided Fine-tuning of SpeechLLMs for Multi-Aspect, Multi-Rater L2 Reading-Speech Assessment](https://md.research.elamur.ai/free/en/papers/parikh-2026-rubric-guided-fine-tuning-speechllms/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aditya Kamlesh Parikh, Cristian Tejedor-García, Catia Cucchiarini, et al.*2026 · 1 citation*
74. **[Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images](https://md.research.elamur.ai/free/en/papers/zhang-2026-structured-abstractive-reasoning-multi-modal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichi Zhang, Zhuo Chen, Lingbing Guo, et al.*2026 · 1 citation*
75. **[UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models](https://md.research.elamur.ai/free/en/papers/shi-2026-ultraeval-audio-unified-framework-comprehensive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qundong Shi, Jie Zhou, Biyuan Lin, et al.*2026 · 1 citation*
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77. **[A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German](https://md.research.elamur.ai/free/en/papers/nouri-2026-novel-synthetic-dataset-few-shot/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Shiva Banasaz Nouri, Elena Leitner, Julian Moreno-Schneider, et al.*2026*
78. **[Afrispeech Semantics: Evaluating Audio–Semantic Reasoning in Spoken Language Models Across Domains and Accents](https://md.research.elamur.ai/free/en/papers/okocha-2026-afrispeech-semantics-evaluating-audio-semantic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chibuzor Okocha, Christan Grant*2026*
79. **[AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments](https://md.research.elamur.ai/free/en/papers/xi-2026-agentgym2-benchmarking-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiheng Xi, Dingwen Yang, Jiaqi Liu, et al.*2026*
80. **[Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-transcription-unified-audio-schema/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Linhao Zhang, Yuhan Song, Aiwei Liu, et al.*2026*
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82. **[DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2026-diffa-2-practical-diffusion-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaming Zhou, Xuxin Cheng, Shiwan Zhao, et al.*2026*
83. **[HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2026-halluaudio-comprehensive-benchmark-hallucination-detection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Feiyu Zhao, Yiming Chen, Wenhuan Lu, et al.*2026*
84. **[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*
85. **[Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/luo-2026-latent-attention-denoising-training-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiwen Luo, Siyu Jiang, Weilong Jiang, et al.*2026*
86. **[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*
87. **[Now You Hear Me: Audio Narrative Attacks Against Large Audio–Language Models](https://md.research.elamur.ai/free/en/papers/yu-2026-now-you-hear-me-audio/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ye Yu, Haibo Jin, Yaoning Yu, et al.*2026*
88. **[ORCA: Open-ended Response Correctness Assessment for Audio Question Answering](https://md.research.elamur.ai/free/en/papers/sedlacek-2026-orca-open-ended-response-correctness/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Šimon Sedláček, Sara Barahona, Cecilia Bolaños, et al.*2026*
89. **[Protecting Bystander Privacy via Selective Hearing in Audio LLMs](https://md.research.elamur.ai/free/en/papers/zhan-2026-protecting-bystander-privacy-selective-hearing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiao Zhan, Guangzhi Sun, Jose Such, et al.*2026*
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91. **[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*
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94. **[Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception](https://md.research.elamur.ai/free/en/papers/wan-2026-speech-hands-self-reflection-voice/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian, et al.*2026*
95. **[Taming Object Hallucinations with Verified Atomic Confidence Estimation](https://md.research.elamur.ai/free/en/papers/liu-2026-taming-object-hallucinations-verified-atomic/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jiarui Liu, Weihao Xuan, Zhijing Jin, et al.*2026*
96. **[Thesis Proposal: When Does an Agent Know It Is Lost? Confidence Trajectory Analysis for Tool-Using LLMs](https://md.research.elamur.ai/free/en/papers/mao-2026-thesis-proposal-when-does-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhenjiang Mao*2026*
97. **[Think Smart, Not Hard: Difficulty Adaptive Reasoning for Large Audio Language Models](https://md.research.elamur.ai/free/en/papers/sheng-2026-think-smart-not-hard-difficulty/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Sheng, Shilin Zhou, Chen Gong, et al.*2026*
98. **[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*
99. **[WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem](https://md.research.elamur.ai/free/en/papers/wang-2026-wenetspeech-wu-datasets-benchmarks-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengyou Wang, Mingchen Shao, Jingbin Hu, et al.*2026*
100. **[When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs](https://md.research.elamur.ai/free/en/papers/liu-2026-when-seeing-is-not-enough/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongcheng Liu, Pingjie Wang, Yuhao Wang, et al.*2026*

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