5 citations · 6 across the 5 of their papers we have counts for
8 papers · 1 filter
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
Yongqiang Chen, Gang Niu, James Cheng +2
Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervis…
How Interpretable Are Interpretable Graph Neural Networks?
Yongqiang Chen, Yatao Bian, Bo Han +1
Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention…
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
Chenxi Liu, Yongqiang Chen, Tongliang Liu +4
Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice…
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
Binghui Xie, Yatao Bian, Kaiwen zhou +5
Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodolog…
Enhancing Evolving Domain Generalization through Dynamic Latent Representations
Binghui Xie, Yongqiang Chen, Jiaqi Wang +4
Domain generalization is a critical challenge for machine learning systems. Prior domain generalization methods focus on extracting domain-invariant features across several station…
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes
Yongqiang Chen, Binghui Xie, Kaiwen Zhou +3
In-context learning (ICL) refers to the ability of a model to condition on a few in-context demonstrations (input-output examples of the underlying task) to generate the answer for…