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Jinhong Yu

2 accepted papers

2025

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains

EMNLP 2025

Large Language Models (LLMs) have demonstrated an impressive level of general knowledge. However, they often struggle in highly specialized and sensitive domains such as drug discovery and rare disease research due to the lack of expert knowledge, which is often costly to obtain. In this paper, we p

2025

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning

EMNLP 2025

Instruction tuning has underscored the significant potential of large language models (LLMs) in producing more human controllable and effective outputs in various domains. In this work, we focus on the data selection problem for task-specific instruction tuning of LLMs. Prevailing methods primarily