Papers by Alimohammad Beigi
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)
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Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu
| Challenge: | Existing surveys focus on LLMs' specific utility for data annotation and synthesis. |
| Approach: | They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations . |
| Outcome: | The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information. |