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Zepeng Lin

2 accepted papers

2025

APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training

ACL 2025finding

Large Language Models (LLMs) often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. Maintaining a balance between domain-specific enhancements and general model utility is a key challenge. This paper proposes a novel approach named APT…

2025

SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models

EMNLP 2025

Existing alignment methods for preference optimization of large language models (LLMs) aim to enhance model performance by utilizing pairs of positive and negative samples. However, due to the limited capacity of models in scoring or generating responses, the quality of positive and negative samples

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