Papers by Wenjie Pang
Media Source Matters More Than Content: Unveiling Political Bias in LLM-Generated Citations (2025.emnlp-main)
Copied to clipboard
| Challenge: | generative search engines rely on in-line citations as the key gateway to original webpages . a recent study shows that LLMs tend to cite left-leaning sources at higher rates compared to traditional retrieval systems . |
| Approach: | They construct a dataset of news articles labeled with left- or right-leaning stances . they find that LLMs tend to cite left-leansing sources at higher rates than traditional retrieval systems . |
| Outcome: | The proposed dataset shows that LLMs tend to cite left-leaning sources at higher rates than traditional retrieval systems. |
A Study of Implicit Ranking Unfairness in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated superior ability to serve as ranking models, but they will exhibit discriminatory ranking behaviors based on users’ sensitive attributes (gender). |
| Approach: | They propose an evaluation method to investigate the severity of implicit ranking unfairness and a pair-wise regression method to conduct fair-aware data augmentation for LLM fine-tuning. |
| Outcome: | The proposed method outperforms existing methods in ranking fairness, achieving this with only a small reduction in accuracy. |
Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading. |
| Approach: | They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets. |
| Outcome: | The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans. |
The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis (2026.acl-long)
Copied to clipboard
Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Jingang Wang, Fei Sun, Huawei Shen, Xueqi Cheng
| Challenge: | Explicit reasoning trajectories increase performance but often trigger overthinking . despite its importance, this study examines how each step of reasoning affects the final outcome . |
| Approach: | They propose a Reasoning Completion Point Detector that detects the RCP by monitoring rank dynamics of termination tokens. |
| Outcome: | The proposed method reduces token usage by up to 44% while preserving accuracy. |