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cs.CL2025
Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
Dingzriui Wang, Xuanliang Zhang, Keyan Xu +3
In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across mo…
cs.CL2025
What Should Feature Distillation Transfer in LLMs? A Task-Tangent Geometry View
Khouloud Saadi, Di Wang
Feature-based knowledge distillation aims to transfer intermediate representations from a teacher LLM model to a student. Existing approaches typically rely on direct feature match…