activity
20232026
most citedTowards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph

16 citations · 17 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling

Yujie Zhang, Weikang Yuan, Zhuoren Jiang +1

Pluralistic alignment is essential for adapting large language models (LLMs) to the diverse preferences of individuals and minority groups. However, existing personalized reward mo…

cs.CL2025

AlignSurvey: A Comprehensive Benchmark for Human Preferences Alignment in Social Surveys

Chenxi Lin, Weikang Yuan, Zhuoren Jiang +5

Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Yet traditional surveys face persistent challe…

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.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.AI2024★ 1 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★ 16 cited

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…