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cs.CL2026
Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework
Xilai Ma, Liye Zhao, Weijun Yao +3
Large Language Model (LLM) personalization aims to align model behaviors with individual user preferences. Existing methods often focus on isolated user histories, neglecting the e…
cs.CL2026
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
Wu Li, Yigeng Zhou, Zesheng Shi +3
While recent self-training approaches have reduced reliance on human-labeled data for aligning LLMs, they still face critical limitations: (i) sensitivity to synthetic data quality…
cs.CL2025
STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment
Jiaqian Li, Qisheng Hu, Jing Li +1
In-Context Learning (ICL) has become a powerful paradigm that enables LLMs to perform a wide range of tasks without task-specific fine-tuning. However, the effectiveness of ICL hea…