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cs.CL2025
Sensitivity Meets Sparsity: The Impact of Extremely Sparse Parameter Patterns on Theory-of-Mind of Large Language Models
Yuheng Wu, Wentao Guo, Zirui Liu +3
This paper investigates the emergence of Theory-of-Mind (ToM) capabilities in large language models (LLMs) from a mechanistic perspective, focusing on the role of extremely sparse…
cs.CL2024
Do LLMs Know to Respect Copyright Notice?
Jialiang Xu, Shenglan Li, Zhaozhuo Xu +1
Prior study shows that LLMs sometimes generate content that violates copyright. In this paper, we study another important yet underexplored problem, i.e., will LLMs respect copyrig…
cs.CL2024
Measuring Copyright Risks of Large Language Model via Partial Information Probing
Weijie Zhao, Huajie Shao, Zhaozhuo Xu +2
Exploring the data sources used to train Large Language Models (LLMs) is a crucial direction in investigating potential copyright infringement by these models. While this approach…