Papers by Haorui Wang
Interesting Culture: Social Relation Recognition from Videos via Culture De-confounding (2025.findings-emnlp)
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| Challenge: | a culturally-specific cultural context can be used to train relationship recognition models . cultural confounding factors can be learned, limiting ability to recognize social relationships in different cultures. |
| Approach: | They propose a culturally-based model that mitigates the influence of culture . they also construct a video social relation recognition dataset to facilitate discussion . |
| Outcome: | The proposed model surpasses state-of-the-art methods on several datasets. |
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific fine-tuning or prompt engineering. |
| Approach: | They propose a task that challenges LLMs to identify the locations of mines based on numerical clues provided by adjacent cells. |
| Outcome: | The proposed task requires an understanding of each cell’s state, discerning spatial relationships between clues and mines, and strategizing actions based on logical deductions drawn from the arrangement of the cells. |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
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Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
SegTune: Structured and Fine-Grained Control for Song Generation (2026.acl-long)
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Yuejiao Wang, Zihao Ji, Pengfei Cai, Xu Li, Haorui Zheng, Zewen Song, Zhongliang Liu, Chen Zhang, Pengfei Wan
| Challenge: | Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. |
| Approach: | They propose a framework that allows users to specify local musical descriptions aligned to song segments. |
| Outcome: | The proposed framework outperforms baselines in musicality and controllability. |
Synergizing Multimodal Temporal Knowledge Graphs and Large Language Models for Social Relation Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have limited capacity to model complex graph-structured relationships. |
| Approach: | They propose a low-coupling method synergizing multimodal temporal Knowledge Graphs and Large Language Models for social relation reasoning. |
| Outcome: | The proposed method exhibits state-of-the-art performance in social relation recognition . it bridges the gap between KGs and LLMs and will be released after acceptance . |