49 citations · 116 across the 26 of their papers we have counts for
8 papers · 1 filter
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
Yajing Wang, Zongwei Luo, Jingzhe Wang +3
Prompt optimization automatically refines prompting expressions, unlocking the full potential of LLMs in downstream tasks. However, current prompt optimization methods are costly t…
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation
Shuhan Guo, Yatao Bian, Ruibing Wang +3
The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications. However, most…
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…
HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment
Yongqiang Chen, Quanming Yao, Juzheng Zhang +2
Recently, there has been a surge of interest in extending the success of large language models (LLMs) from texts to molecules. Most existing approaches adopt a graph neural network…
A Sober Look at the Robustness of CLIPs to Spurious Features
Qizhou Wang, Yong Lin, Yongqiang Chen +3
Large vision language models, such as CLIP, demonstrate impressive robustness to spurious features than single-modal models trained on ImageNet. However, existing test datasets are…
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…