Papers by Jianbo Yuan
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
DavIR: Data Selection via Implicit Reward for Large Language Models (2025.acl-long)
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Haotian Zhou, Tingkai Liu, Qianli Ma, Yufeng Zhang, Jianbo Yuan, Pengfei Liu, Yang You, Hongxia Yang
| Challenge: | 6% of Alpaca dataset selected with DavIR can steer both LLaMA and Gemma models to produce superior performance compared to the same models trained on the full 52K dataset. |
| Approach: | They propose a model-based data selection method for post-training Large Language Models . they generalize Reducible Holdout Loss to core-set selection problem of causal language modeling . |
| Outcome: | The proposed method can steer both LLaMA and Gemma models to superior performance compared to the same models trained on the full 52K dataset. |
An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing (2024.acl-long)
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Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang, Xuanwen Huang, Xuwu Wang, Jingjing Xu, Jianbo Yuan, Hongxia Yang, Fei Wu, Yang Yang
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across a wide spectrum of tasks, but performance and reliability in certain specialized domains still fall short of expectations. |
| Approach: | They propose a unified generalist framework that facilitates seamless integration of multiple expert LLMs. |
| Outcome: | The proposed framework outperforms existing multi-LLM collaboration paradigms across six diverse expert domains. |