2 citations · 2 across the 9 of their papers we have counts for
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Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning
Yixin Wan, Anil Ramakrishna, Kai-Wei Chang +2
Large Language Model (LLM) unlearning has recently gained significant attention, driven by the need to remove unwanted information, such as private, sensitive, or copyrighted conte…
SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
Anil Ramakrishna, Yixin Wan, Xiaomeng Jin +6
We introduce SemEval-2025 Task 4: unlearning sensitive content from Large Language Models (LLMs). The task features 3 subtasks for LLM unlearning spanning different use cases: (1)…
Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM
Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2
Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various L…
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy
Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2
Decoding methods for large language models (LLMs) usually struggle with the tradeoff between ensuring factuality and maintaining diversity. For example, a higher p threshold in the…