3 papers
cs.CL2025
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Daniela Gottesman, Alon Gilae-Dotan, Ido Cohen +4
Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models turn data into representations o…
cs.CL2025
Precise In-Parameter Concept Erasure in Large Language Models
Yoav Gur-Arieh, Clara Suslik, Yihuai Hong +2
Large language models (LLMs) often acquire knowledge during pretraining that is undesirable in downstream deployments, e.g., sensitive information or copyrighted content. Existing…
cs.CL2025
Enhancing Automated Interpretability with Output-Centric Feature Descriptions
Yoav Gur-Arieh, Roy Mayan, Chen Agassy +2
Automated interpretability pipelines generate natural language descriptions for the concepts represented by features in large language models (LLMs), such as plants or the first wo…