Papers by Tieniu Tan
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models (2024.findings-acl)
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| Challenge: | Object hallucination has been an Achilles’ heel which hinders the broader applications of large vision-language models (LVLMs). |
| Approach: | They propose a logical closed loop-based framework for Object Hallucination Detection and Mitigation that uses logical consistency probing to raise questions with logical correlations to determine hallucinations. |
| Outcome: | The proposed method can be applied to all existing LVLMs and is effective and general. |
Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models (2025.findings-acl)
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| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities across visual tasks, yet they remain hindered by the persistent challenge of hallucinations. |
| Approach: | They propose a novel approach that dynamically adapts decoding strategies by evaluating the correctness of the model’s attention on image tokens to distinguish the correct attention. |
| Outcome: | Extensive experiments show that the proposed approach outperforms existing decoding methods across multiple mainstream benchmarks, effectively mitigating hallucinations in LVLMs. |
VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation (2025.findings-acl)
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Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu, Di Zhang, Tieniu Tan
| Challenge: | Existing studies have not identified a link between video caption evaluation and T2V generation. |
| Approach: | They propose a video caption evaluation scheme specifically designed for T2V generation that integrates video annotation with caption evaluation. |
| Outcome: | The proposed system is agnostic to any particular caption format and can be used for training. |
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025.acl-long)
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| Challenge: | Recent studies have shown that scaling test-time compute can also effectively improve reasoning. |
| Approach: | They propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times. |
| Outcome: | The proposed method significantly improves the scaling performance of majority voting on large language models. |
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026.acl-long)
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| Challenge: | Existing TTRL methods rely on positive pseudo-labeling strategies to enhance reasoning capabilities. |
| Approach: | They propose a test-time reinforcement learning framework that mitigates label noise amplification by deriving pseudo-rewards from majority voting consensus. |
| Outcome: | The proposed framework mitigates label noise amplification by implementing selective positive pseudo-labeling and entropy-gated negative p-labeled pruning. |
SHARP: Steering Hallucination in LVLMs via Representation Engineering (2025.emnlp-main)
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Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
| Challenge: | Large Vision-Language Models (LVLMs) generate responses that are plausible but incorrect or unsupported—commonly referred to as hallucinations. |
| Approach: | They propose a representation-level intervention framework that modulates hallucination-related features during inference by probing their encoded features. |
| Outcome: | The proposed framework reduces hallucinations while maintaining the performance and generalization capabilities of Large Vision-Language Models (LVLMs). |
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)
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Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan
| Challenge: | Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. |
| Approach: | They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector. |
| Outcome: | The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE. |