Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing tasks. However, these models have been shown to harbor inherent societal biases, or stereotypes, which can adversely affect their performance in their many downstream applications. In this paper, we introduce a novel, purely prompt-based approach to uncover hidden stereotypes within any arbitrary LLM. Our approach dynamically generates a knowledge representation of internal stereotypes, enabling the identification of biases encoded within the LLM's internal knowledge. By illuminating the biases present in LLMs and offering a systematic methodology for their analysis, our work contributes to advancing transparency and promoting fairness in natural language processing systems.
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language Model
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Tongji University（同济大学）
Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models
Center for Language and Speech Processing (CLSP)（语言与语音处理中心）；Johns Hopkins University（约翰霍普金斯大学）；Group on Language, Audio & Music (GLAM)（语言、音频与音乐小组）；Imperial College London（伦敦帝国学院）
Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs
Data Science Center ScaDS.AI, Universität Leipzig（ScaDS.AI数据科学中心，莱比锡大学）；Department of Artificial Intelligence, Fraunhofer Heinrich-Hertz-Institute（人工智能系，弗劳恩霍夫 Heinrich-Hertz 研究所）；Department of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学系，柏林技术大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Centre of eXplainable Artificial Intelligence, Technological University Dublin（可解释人工智能中心，都柏林技术大学）
