4 citations · 7 across the 10 of their papers we have counts for
12 papers
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs
Shuyuan Zheng, Yun-seo Lee, Xing Gao +7
Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction. In legal practice, this reasoning is shap…
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation
Hui Huang, Jie Zhou, Hongli Zhou +7
Reward models are central to preference alignment for legal tasks, yet existing reward model benchmarks are still primarily centered on general-domain preferences. In this work, we…
When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning
Yiqian Huang, Shuyuan Zheng, Yang Zhou +7
Legal reasoning requires identifying which version of a statute applies to a case, typically based on when the legally relevant events occurred rather than when the case is adjudic…
Nonlinearity-Aware LoRA: Structured Gate Adaptation under Low-Rank Constraints
Shuai Yuan, Sudong Cai, Bingzhi Chen +4
Low-rank adaptation (LoRA) is commonly viewed as an update-space approximation to full fine-tuning, yet this view is incomplete for self-gated Transformer feed-forward networks. In…
Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning
Chuang Ma, Qianying Liu, Tomoyuki Obuchi +6
Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly attended visual information. We i…
Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
Xiqi Hao, Zengqing Wu, Yu-Xuan Qiu +4
Multi-agent debate (MAD) among large language models (LLMs) is often judged by answer changes and convergence, yet these outcomes alone cannot establish that peers improved the ans…
ShapleyLaw: A Game-Theoretic Approach to Multilingual Scaling Laws
Xuyang Cao, Qianying Liu, Chuan Xiao +7
In multilingual pretraining, the test loss of a pretrained model is heavily influenced by the proportion of each language in the pretraining data, namely the \textit{language mixtu…
Seven Security Challenges in Cross-domain Multi-agent LLM Systems
Ronny Ko, Jiseong Jeong, Shuyuan Zheng +4
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimizatio…
Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction
Junkai Liu, Yujie Tong, Hui Huang +7
Legal judgment prediction (LJP), which enables litigants and their lawyers to forecast judgment outcomes and refine litigation strategies, has emerged as a crucial legal NLP task.…
Privacy-Enhanced Database Synthesis for Benchmark Publishing (Technical Report)
Yunqing Ge, Jianbin Qin, Shuyuan Zheng +7
Benchmarking is crucial for evaluating a DBMS, yet existing benchmarks often fail to reflect the varied nature of user workloads. As a result, there is increasing momentum toward c…
Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents
Zengqing Wu, Run Peng, Shuyuan Zheng +6
Large Language Models (LLMs) have increasingly been utilized in social simulations, where they are often guided by carefully crafted instructions to stably exhibit human-like behav…
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
Zengqing Wu, Run Peng, Xu Han +3
Computer simulations offer a robust toolset for exploring complex systems across various disciplines. A particularly impactful approach within this realm is Agent-Based Modeling (A…