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cs.LG2025
Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning
Vivswan Shah, Randy Cogill, Hanwei Yue +2
Fine-tuning LLMs for classification typically maps inputs directly to labels. We ask whether attaching brief explanations to each label during fine-tuning yields better models. We…
cs.AI2025
Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
Andrea Wynn, Metod Jazbec, Charith Peris +4
Large language models (LLMs) can be influenced by harmful or irrelevant context, which can significantly harm model performance on downstream tasks. This motivates principled desig…
cs.CL2025
Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models
Taha Entesari, Arman Hatami, Rinat Khaziev +2
Large Language Models (LLMs) deployed in real-world settings increasingly face the need to unlearn sensitive, outdated, or proprietary information. Existing unlearning methods typi…