Papers by Huayi Liu

2 papers
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)

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Challenge: Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance .
Approach: They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal.
Outcome: The proposed method surpasses state-of-the-art methods for task-specific instruction tuning.
MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation (2025.emnlp-industry)

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Challenge: Recent studies show that LLM-based agents exhibit superior moral and emotional language performance compared to humans, raising expectations for their deployment in persuasive tasks.
Approach: They propose a framework for generating persuasive multi-turn dialogues via agent self-play using user agents designed to simulate diverse persona-driven behaviors, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes.
Outcome: The proposed framework significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) .

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