Papers by Jiayi Gui
LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models (2025.findings-acl)
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Jiayi Gui, Yiming Liu, Jiale Cheng, Xiaotao Gu, Xiao Liu, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang
| Challenge: | Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. |
| Approach: | They propose a benchmark to evaluate the rule-based logical reasoning capabilities of Large Language Models (LLMs) they create simulated scenarios in which models execute or plan operations to achieve specific outcomes. |
| Outcome: | The proposed benchmark evaluates the performance of large language models on a variety of scenarios with varying difficulty levels. |
P4: Plug-and-Play Discrete Prompting for Large Language Models Personalization (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit impressive capabilities in following instructions, but manually prompting them to exhibit certain personalities may result in sub-optimal performance. |
| Approach: | They propose a plug-and-play prompting method to manipulate Large Language Models with distinct human-like personality traits by appending discrete personalized suffixes to query or dialog histories and focusing exclusively on influential tokens. |
| Outcome: | The proposed method outperforms other prompting methods and model editing methods on four models ranging from 1.1B to 13B and achieves 79.9% accuracy in customizing LLMs’ personalities. |
AIDER: a Robust and Topic-Independent Framework for Detecting AI-Generated Text (2025.coling-main)
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| Challenge: | Current fine-tuned detectors lack robustness against adversarial attacks and struggle with out-of-distribution topics, limiting their practical applicability. |
| Approach: | They propose a topic-independent framework for detecting AI-generated text . it leverages the ALBERT model for topic content disentanglement, enhancing transferability to unseen topics. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in detecting human-written and AI-generated content under adversarial and topic-varied conditions. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |