Papers by Zhiting Fan
FairSteer: Inference Time Debiasing for LLMs with Dynamic Activation Steering (2025.findings-acl)
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| Challenge: | Existing prompt-based debiasing methods exhibit instability due to sensitivity to prompt changes . fine-tuning-based techniques incur substantial computational overhead and catastrophic forgetting . |
| Approach: | They propose a debiasing framework that encodes fairness-related features into separable directions in the hidden activation space. |
| Outcome: | The proposed framework performs inference-time debiasing without requiring retraining or prompt design . it detects bias signatures in activations and then computes debiased steering vectors . the proposed framework is available to download in the u.s. |
BiasGuard: A Reasoning-Enhanced Bias Detection Tool for Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods for identifying bias in LLM-generated content face limitations . existing methods rely on pattern-based learning, which makes it challenging to understand intentions . |
| Approach: | They propose a bias detection tool that explicitly analyzes inputs and reasons through fairness specifications to provide accurate judgments. |
| Outcome: | The proposed tool outperforms existing tools and improves accuracy and reduces over-fairness misjudgments. |
BiasAlert: A Plug-and-play Tool for Social Bias Detection in LLMs (2024.emnlp-main)
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| Challenge: | Existing evaluation approaches rely on fixed-form outputs and cannot adapt to flexible open-text generation scenarios. |
| Approach: | They propose a plug-and-play tool to detect social bias in open-text LLMs. |
| Outcome: | Extensive experiments show that BiasAlert outperforms state-of-the-art methods in detecting bias in open-text generation scenarios. |
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)
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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao
| Challenge: | High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. |
| Approach: | They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth. |
| Outcome: | the proposed pipeline outperforms 14 leading baselines on 16 benchmarks. |
Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning (2025.emnlp-industry)
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Ruizhe Chen, Tianze Luo, Zhiting Fan, Heqing Zou, Zhaopeng Feng, Guiyang Xie, Hansheng Zhang, Zhuochen Wang, Zuozhu Liu, Zhang Huaijian
| Challenge: | Existing methods for video temporal grounding suffer from limited temporal awareness and poor generalization. |
| Approach: | They propose a two-stage training framework that integrates supervised fine-tuning with reinforcement learning to improve both the accuracy and robustness of VTG models. |
| Outcome: | The proposed training framework outperforms existing models on multiple benchmarks on open-domain and challenging scenarios. |