173 citations · 644 across the 150 of their papers we have counts for
8 papers · 1 filter
Anticipating Social Consequences with Distributional World Models
Viet-Thanh Pham, Thuy-Trang Vu, Zhuang Li +2
Social world models predict how interactions develop after an action, helping agents negotiate, cooperate and mediate. In these situations, one action can lead to several plausible…
LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations
Viet-Thanh Pham, Lizhen Qu, Thuy-Trang Vu +2
Large language models (LLMs) are increasingly deployed as autonomous agents, yet evaluations focus primarily on task success rather than cultural appropriateness or evaluator relia…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning
Vy Vo, Lizhen Qu, Tao Feng +6
Identifying cause-and-effect relationships is critical to understanding real-world dynamics and ultimately causal reasoning. Existing methods for identifying event causality in NLP…
ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning
Linhao Luo, Jiaxin Ju, Bo Xiong +3
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graph…
Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain
Fatemeh Shiri, Teresa Wang, Shirui Pan +5
International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely relate…
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Vy Vo, Trung Le, Van Nguyen +4
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach i…
Neural-Symbolic Commonsense Reasoner with Relation Predictors
Farhad Moghimifar, Lizhen Qu, Yue Zhuo +2
Commonsense reasoning aims to incorporate sets of commonsense facts, retrieved from Commonsense Knowledge Graphs (CKG), to draw conclusion about ordinary situations. The dynamic na…