most citedCausal Evaluation of Language Models

3 citations · 3 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL2025

DEPO: Dual-Efficiency Preference Optimization for LLM Agents

Sirui Chen, Mengshi Zhao, Lei Xu +5

Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes…

cs.LG2025

UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization

Kai Yang, Zelin Zhu, Chengtao Jian +4

Urban general intelligence (UGI) refers to the capacity of AI systems to autonomously perceive, reason, and act within dynamic and complex urban environments. In this paper, we int…

cs.CV2025

On the Robustness of Human-Object Interaction Detection against Distribution Shift

Chi Xie, Shuang Liang, Jie Li +4

Human-Object Interaction (HOI) detection has seen substantial advances in recent years. However, existing works focus on the standard setting with ideal images and natural distribu…

cs.CL2025

Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks

Sirui Chen, Shuqin Ma, Shu Yu +3

Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language m…

cs.CL2024

From Imitation to Introspection: Probing Self-Consciousness in Language Models

Sirui Chen, Shu Yu, Shengjie Zhao +1

Self-consciousness, the introspection of one's existence and thoughts, represents a high-level cognitive process. As language models advance at an unprecedented pace, a critical qu…

cs.CL20241 cited

CLEAR: Can Language Models Really Understand Causal Graphs?

Sirui Chen, Mengying Xu, Kun Wang +4

Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive…