3 papers
cs.CL2025
Mechanistic Fine-tuning for In-context Learning
Hakaze Cho, Peng Luo, Mariko Kato +2
In-context Learning (ICL) utilizes structured demonstration-query inputs to induce few-shot learning on Language Models (LMs), which are not originally pre-trained on ICL-style dat…
cs.CL2025
Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations
Mariko Kato, Hakaze Cho, Yoshihiro Sakai +1
The performance of In-Context Learning (ICL) is highly sensitive to the selected demonstrations. Existing approaches to demonstration selection optimize different objectives, yield…
cs.CL2024
Revisiting In-context Learning Inference Circuit in Large Language Models
Hakaze Cho, Mariko Kato, Yoshihiro Sakai +1
In-context Learning (ICL) is an emerging few-shot learning paradigm on Language Models (LMs) with inner mechanisms un-explored. There are already existing works describing the inne…