Papers by Yiwen Guo
Code Representation Pre-training with Complements from Program Executions (2024.emnlp-industry)
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| Challenge: | Existing languages have syntactic representations of code to improve code intelligence, but they are difficult to learn from code. |
| Approach: | They propose to embed dynamic information of programs revealed by their test cases into feature representations of code as complements. |
| Outcome: | The proposed method yields 6%/19% mAP improvements over its masked language modeling counterparts. |
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)
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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored. |
| Approach: | They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations. |
| Outcome: | The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. |
Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing (2026.acl-long)
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Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai, Xiaowei Shi, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large vision language models have impressive reasoning capabilities across complex multimodal tasks. |
| Approach: | They propose to use distribution-reshaping and trajectory-rebalancing to improve visual reasoning capabilities. |
| Outcome: | Experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models show that their methods outperform baselines by 3.86 points. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation (2023.findings-emnlp)
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| Challenge: | Large vision-language models are often not open-source due to preventing abuse or commercial factors. |
| Approach: | They propose a method for parameter-efficient fine-tuning to improve model accessibility . large models are often not open-source due to preventing abuse or commercial factors . they propose implementing a lightweight adapter over the output feature of an inaccessible model . |
| Outcome: | The proposed methods improve on 11 benchmarks and are made publicly available. |
Understanding Programs by Exploiting (Fuzzing) Test Cases (2023.findings-acl)
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| Challenge: | Semantic understanding of programs has attracted great attention in the community . large language models (LLMs) are capable of learning contextual information from data at scale . |
| Approach: | They propose to incorporate a relationship between inputs and possible outputs into learning for achieving a deeper semantic understanding of programs. |
| Outcome: | The proposed method outperforms current state-of-the-art on two programming tasks and outperformed current state of the art by large margins. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
The Program Testing Ability of Large Language Models for Code (2024.emnlp-industry)
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| Challenge: | Recent development of large language models (LLMs) for code shows promise in achieving code intelligence. |
| Approach: | They explore the ability of large language models to generate automated test cases . they show +11.77% and +4.22% higher code pass rates on HumanEval+ . |
| Outcome: | The proposed models show higher pass rates on humanEval+ compared with the current state-of-the-art models. |