Papers by Jiexi Liu
Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models . |
| Approach: | They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength. |
| Outcome: | The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks. |
Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)
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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang
| Challenge: | a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations . |
| Approach: | They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems. |
| Outcome: | The proposed toolkit reveals critical robustness issues in state-of-the-art models. |
Multiple Instance Learning for Offensive Language Detection (2022.findings-emnlp)
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| Challenge: | Existing approaches to detect offensive content are expensive and require massive manual effort. |
| Approach: | They propose an approach capable of utilizing the bag-level labeled data for offensive language detection by an annotation-based model. |
| Outcome: | The proposed model can detect offensive language on both bag-level and sentence level. |
Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for locating and editing static knowledge are costly and risk catastrophic forgetting or error. |
| Approach: | They propose a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update. |
| Outcome: | Experiments on standard benchmarks show that FiDAL improves editing effectiveness and knowledge preservation across multiple editing methods. |