3 papers
cs.IT2026
A Theoretical Interpretation of In-Context Learning via Probabilistic Modeling
Zhenyu Liu, Huaze Tang, Shao-Lun Huang
In-context learning (ICL) is an emerging paradigm that employs the semantic information inherent in large language models (LLMs) for generating answers to user queries. While the r…
cs.LG2026
Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework
Qingyue Zhang, Chang Chu, Haohao Fu +5
In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typical…
cs.IT2025
On Theoretical Interpretations of Concept-Based In-Context Learning
Huaze Tang, Tianren Peng, Shao-lun Huang
In-Context Learning (ICL) has emerged as an important new paradigm in natural language processing and large language model (LLM) applications. However, the theoretical understandin…