Papers by Shijia Huang
Learning Preference Model for LLMs via Automatic Preference Data Generation (2023.emnlp-main)
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| Challenge: | Existing training methods for large language models rely on human-annotated data. |
| Approach: | They propose to learn the preference model for LLMs via automatic preference data generation (AutoPM) using HHH-guided preference data, they show reliability and potential . |
| Outcome: | The proposed approach enables LLMs to learn human preferences and align with human values. |
Enhancing Temporal Modeling of Video LLMs via Time Gating (2024.findings-emnlp)
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| Challenge: | Existing Video Large Language Models neglect temporal information in video data, leading to struggles with temporal-aware video understanding. |
| Approach: | They propose a Time Gating Video LLM (TG-Vid) that employs a time gating module to enhance temporal modeling. |
| Outcome: | The proposed model outperforms existing Large Language Models on video-and-language tasks and ablation studies show that the model outpersforms the existing models. |
FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning (2026.acl-short)
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| Challenge: | Figurative language recognition challenges distinguishing between fine-grained rhetorical categories . existing approaches are framed as single-category binary classifiers . |
| Approach: | They propose a framework that integrates prompt-based knowledge injection with supervised contrastive learning to enforce explicit class distinctions. |
| Outcome: | The proposed framework achieves competitive performance on a four-way sentence-level classification task. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing. |
| Approach: | They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy. |
| Outcome: | CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding. |