As language models are increasingly included in human-facing machine learning tools, bias against demographic subgroups has gained attention. We propose FineDeb, a two-phase debiasing framework for language models that starts with contextual debiasing of embeddings learned by pretrained language models. The model is then fine-tuned on a language modeling objective. Our results show that FineDeb offers stronger debiasing in comparison to other methods which often result in models as biased as the original language model. Our framework is generalizable for demographics with multiple classes, and we demonstrate its effectiveness through extensive experiments and comparisons with state of the art techniques. We release our code and data on GitHub.
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing
School of Computer Science, Peking University（北京大学计算机科学学院）；MOE Key Laboratory of Computational Linguistics, Peking University（北京大学教育部计算语言学重点实验室）；National Key Laboratory for Multimedia Information Processing, Peking University（北京大学国家多媒体信息处理重点实验室）
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学信息与语言处理中心）；Institute of Social and Cultural Anthropology, LMU Munich（慕尼黑大学社会与文化人类学研究所）
UGID: Unified Graph Isomorphism for Debiasing Large Language Models
University of Electronic Science and Technology of China（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学）；South China University of Technology（华南理工大学）
Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models
Research Center for Social Computing and Interactive Robotics（社会计算与交互机器人研究中心）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Tsinghua University（清华大学）；Beijing Academy of（北京学院（文本不完整，仅提取现有内容））；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National Research Center for Information Science Technology（国家信息科学技术研究中心）；Microsoft Research Asia（微软亚洲研究院）；International Innovation Center of Tsinghua University（清华大学国际创新中心）
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation
Institute for Logic, Language and Computation, University of Amsterdam（逻辑、语言与计算研究所，阿姆斯特丹大学）；Center for Language and Cognition, University of Groningen（语言与认知中心，格罗宁根大学）
Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
School of Informatics, Xiamen University, China（厦门大学信息学院）；vivo AI Lab, China（vivo AI实验室）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室（厦门大学），文化部，中国）
Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness
Department of Experimental Psychology, University of Oxford（心理学实验系，牛津大学）；Uehiro Oxford Institute, University of Oxford（牛津大学欧胡罗研究所）；Psychology Programme, School of Social Sciences, Nanyang Technological University（南洋理工大学社会科学学院心理学项目）；Department of Communication and Culture, BI Norwegian Business School（BI挪威商学院传播与文化系）
LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
Department of Software \& Microelectronics Peking University Beijing, China；Department of Software \& Microelectronics Peking University Beijing, China hangli\；Department of Software \& Microelectronics Peking University Beijing, China zehua\；Department of Software \& Microelectronics Peking University Beijing, China xiaofan\；Economic Law School China University of Political Science；Peking University Changsha, China
Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Baidu Inc.（百度公司）
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
Massachusetts Institute of Technology（麻省理工学院）；University of Michigan - Ann Arbor（密歇根大学安娜堡分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Harvard University（哈佛大学）；University of the Philippines（菲律宾大学）；Stevens Institute of Technology（史蒂文斯理工学院）
