activity
20232025
most citedCan Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CL2025

LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation

Weikang Yuan, Kaisong Song, Zhuoren Jiang +6

Legal consultation is essential for safeguarding individual rights and ensuring access to justice, yet remains costly and inaccessible to many individuals due to the shortage of pr…

cs.AI2025

Towards Stepwise Domain Knowledge-Driven Reasoning Optimization and Reflection Improvement

Chengyuan Liu, Shihang Wang, Lizhi Qing +7

Recently, stepwise supervision on Chain of Thoughts (CoTs) presents an enhancement on the logical reasoning tasks such as coding and math, with the help of Monte Carlo Tree Search…

cs.LG2024

LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Tianqianjin Lin, Pengwei Yan, Kaisong Song +7

Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper u…

cs.AI20241 cited

Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration

Weikang Yuan, Junjie Cao, Zhuoren Jiang +7

Large Language Models (LLMs) could struggle to fully understand legal theories and perform complex legal reasoning tasks. In this study, we introduce a challenging task (confusing…

cs.LG2023

Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery

Pengwei Yan, Kaisong Song, Zhuoren Jiang +4

While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, hum…

cs.LG2023

Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph

Tianqianjin Lin, Kaisong Song, Zhuoren Jiang +6

Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their in…