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Yuangang Li

4 accepted papers

2026

Mitigating Hallucinations in Large Language Models via Causal Reasoning

AAAI 2026technical

Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relationship between causal reasoning capabilities and such hallucinations. However, existing reasoning approaches in LLMs, suc

Cited by 0SourcePDFScholar
2026

S2D-Align: Shallow-to-Deep Auxiliary Learning for Anatomically-Grounded Radiology Report Generation

AAAI 2026technical

Radiology Report Generation (RRG) aims to automatically generate diagnostic reports from radiology images. To achieve this, existing methods have leveraged the powerful cross-modal generation capabilities of Multimodal Large Language Models (MLLMs), primarily focusing on optimizing cross-modal align

Cited by 0SourcePDFScholar
2025

AD-LLM: Benchmarking Large Language Models for Anomaly Detection

ACL 2025finding

Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural language processing (NLP), AD helps detect issues like spam, misinformation, and unusual user activity. Although large langu…

2025

NLP-ADBench: NLP Anomaly Detection Benchmark

EMNLP 2025

Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively understudied in a natural language processing (NLP) context, limiting its effectiveness in detecting harmful content, phishing