Trustworthy NLP

Our team has contributed significant efforts to advancing Trustworthy NLP, with a focus on developing more robust, fair, and explainable large language models (LLMs). Through innovations in model training, evaluation, and interpretation, we are hoping to develop LLMs that are reliable, unbiased, and transparent. Our work is helping overcome key challenges in deploying LLMs responsibly. Below, we summarize our work on robustness, fairness and explainability of LLMs.

LLM Explainability

• Towards Uncovering How Large Language Model Works: An Explainability Perspective
  Haiyan Zhao, Fan Yang, Bo Shen, Himabindu Lakkaraju, Mengnan Du
  arXiv:2402.10688, 2024

• Quantifying Multilingual Performance of Large Language Models Across Languages
  Zihao Li, Yucheng Shi, Zirui Liu, Fan Yang, Ninghao Liu, Mengnan Du
  arXiv:2404.11553, 2024

• The Impact of Reasoning Step Length on Large Language Models
  Mingyu Jin, Qinkai Yu, Dong shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du
  arXiv:2401.04925, 2024

• Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
  Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
  arXiv:2404.07066, 2024

• Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
  Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu
  arXiv:2403.08946, 2024

• Explainability for Large Language Models: A Survey
  Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du
  ACM Transactions on Intelligent Systems and Technology (TIST)   [Github]

• M⁴: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
  Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, Haoyi Xiong
  NeurIPS Datasets and Benchmarks track, 2023

LLM Fairness

• Enhancing Fairness in In-Context Learning: Prioritizing Minority Samples in Demonstrations
  Jingyu Hu, Mengnan Du
  ICLR Tiny Papers Track, 2024

• A Survey on Fairness in Large Language Models
  Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang
  arXiv:2308.10149, 2023

• Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases
  Yingji Li, Mengnan Du, Xin Wang and Ying Wang
  The 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023
  

LLM Robustness

• Mitigating Shortcuts in Language Models with Soft Label Encoding
  Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du
  COLING, 2024

• Unveiling Project-Specific Bias in Neural Code Models
  Zhiming Li, Yanzhou Li, Tianlin Li, Mengnan Du, Bozhi Wu, Yushi Cao, Xiaofei Xie, Yi Li, Yang Liu
  COLING, 2024

• Shortcut Learning of Large Language Models in Natural Language Understanding
  Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, Xia Hu
  Communications of the ACM (CACM), 2023

• Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
  Mengnan Du, Subho Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu and Ahmed Hassan Awadallah
  The 17th Annual Meeting of the European chapter of the Association for Computational Linguistics (EACL), 2023
  

• Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
  Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun and Xia Hu
  North American Chapter of the Association for Computational Linguistics (NAACL), 2021