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Chengshuai Zhao

7 accepted papers

2026

Low-Rank Few-Shot Node Classification by Node-Level Graph Diffusion

ICLR 2026poster

In this paper, we propose a novel node-level graph diffusion method with low-rank feature learning for few-shot node classification (FSNC), termed Low-Rank Few-Shot Graph Diffusion Model or LR-FGDM. LR-FGDM first employs a novel Few-Shot Graph Diffusion Model (FGDM) as a node-level graph generative…

Cited by 0SourceScholar
2026

Probing to Refine: Reinforcement Distillation of LLM Reasoners via Explanatory Inversion

ICLR 2026poster

Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent advances, distilled models frequently suffer from superficial pattern memorization and subpar generalization. To overcome…

Cited by 0SourceScholar
2025

From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge

EMNLP 2025

Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). Traditional methods, usually matching-based or small model-based, often fall short in open-ended and dynamic scenarios. Recent advancements in Large Language Models (LL

2025

SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention

ACL 2025long

Content analysis breaks down complex and unstructured texts into theory-informed numerical categories. Particularly, in social science, this process usually relies on multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. In this paper, we introduce SCALE, a novel…

2024

Glue pizza and eat rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models

EMNLP 2024main

Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases, improving their performance in applications like fact-checking and information searching. In this paper, we demonstrate a security threat where adversaries can exploit the openne…

Cited by 4SourcePDFScholar
2023

Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules

ICLR 2023poster

Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs) for molecules. Typically, atom types as node attributes are randomly masked, and GNNs are then trained to predict masked types as in AttrMask \citep{hu2020strategies}, following the Masked Language Modeling (MLM)…

2021

CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction

IJCAI 2021poster

Molecular interactions are significant resources for analyzing sophisticated biological systems. Identification of multifarious molecular interactions attracts increasing attention in biomedicine, bioinformatics, and human healthcare communities. Recently, a plethora of methods have been proposed to…