Papers by Dezhi Yu

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.
Vision-Language Models Mistake Head Orientation for Gaze Direction: Nonverbal Conversation Cues (2026.findings-acl)

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Challenge: Where someone looks is a nonverbal communication cue that children and adults readily use.
Approach: They used 1,360 real-world photos to construct evaluation stimuli for Vision-Language Models (VLMs) they found a substantial performance gap between VLMs and humans .
Outcome: The proposed model outperforms existing models in predicting gaze direction using head orientation rather than eye appearance.

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