Papers by Guanyu Chen
MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization. |
| Approach: | They propose a multi-stage graph reasoning framework based on vision-language models that incrementally samples and visualizes task-relevant subgraphs. |
| Outcome: | The proposed framework outperforms existing benchmarks in Graph-related tasks. |
Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances leverage large language models (LLMs) for legal reasoning, but they face high computational costs and information degradation when handling long cases. |
| Approach: | They propose a framework that selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets spanning multiple jurisdictions and languages. |
Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization (2026.acl-long)
Copied to clipboard
| Challenge: | Recent studies attempt to obtain optimal or suboptimal arrangements based on statistical results or using dataset-based search, but these methods increase inference overhead while leaving the model’s inherent order bias unresolved. |
| Approach: | They propose Dual Group Advantage Optimization (DGAO) which aims to improve model accuracy and order stability simultaneously. |
| Outcome: | The proposed method improves model accuracy and order stability while penalizing order-sensitive or incorrect responses. |
Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluations measure privacy-related attitudes or sharing intentions in isolation, making it difficult to determine whether a model’s expressed values jointly predict its downstream data-sharing actions as in real human behaviors. |
| Approach: | They propose a framework that sequentially administers standardized questionnaires for privacy attitudes, prosocialness, and acceptance of data sharing within a bounded, history-carrying session. |
| Outcome: | The proposed model shows that it is stable but model-specific Privacy-PSA-AoDS profiles, and substantial heterogeneity in value-action alignment. |
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)
Copied to clipboard
| Challenge: | Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning. |
| Approach: | They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. |
| Outcome: | The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
Copied to clipboard
Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
GuiLoMo: Allocating Experts and Ranks for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors (2025.findings-emnlp)
Copied to clipboard
Xinrong Chen, Hengyuan Zhang, Yingmin Qiu, Xiao Liang, Ziyue Li, Guanyu Wang, Weiping Li, Tong Mo, Hayden Kwok-Hay So, Ngai Wong
| Challenge: | Low-Rank Adaptation (LoRA) methods are efficient for a large language model with reduced computational costs. |
| Approach: | They propose a layer-wise expert numbers and ranks allocation strategy with GuidedSelection Vectors. |
| Outcome: | The proposed method achieves superior or comparable performance to all baselines on three backbone models. |
Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them (2025.findings-emnlp)
Copied to clipboard
Guanyu Chen, Peiyang Wang, Yizhou Jiang, Yuqian Liu, Chujie Zhao, Ying Fang, Tianren Zhang, Feng Chen
| Challenge: | Existing large language models can perform abstract reasoning tasks but are they actually engaging in rule-based reasoning beyond mere memorization? |
| Approach: | They propose a method to examine whether large language models perform abstract reasoning . they fine-tune the model to learn those contradictory rules and assess its generalization ability . |
| Outcome: | The proposed approach examines whether large language models perform abstract reasoning by altering their original understanding of fundamental rules. |