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20232026
most citedTowards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications

1 citations · 1 across the 10 of their papers we have counts for

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cs.CL2025

DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction

Yiqi Li, Yusheng Liao, Zhe Chen +2

When performing reasoning tasks with user-specific requirements, such as strict output formats, large language models (LLMs) often prioritize reasoning over adherence to detailed i…

cs.LG2025

RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis

Haolin Li, Tianjie Dai, Zhe Chen +4

Clinical diagnosis is a highly specialized discipline requiring both domain expertise and strict adherence to rigorous guidelines. While current AI-driven medical research predomin…

cs.CL2025

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks

Zhe Chen, Yusheng Liao, Zhiyuan Zhu +4

Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world…

cs.CL2025

DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models

YiQiu Guo, Yuchen Yang, Zhe Chen +5

The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. E…

cs.CL2025

RARE: Retrieval-Augmented Reasoning Modeling

Zhengren Wang, Jiayang Yu, Dongsheng Ma +8

Domain-specific intelligence demands specialized knowledge and sophisticated reasoning for problem-solving, posing significant challenges for large language models (LLMs) that stru…

cs.CL2025

MedS: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision

Shuyang Jiang, Yusheng Liao, Zhe Chen +3

Medical language models face critical barriers to real-world clinical reasoning applications. However, mainstream efforts, which fall short in task coverage, lack fine-grained supe…

cs.CL2025★ 1 cited

Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications

Zhe Chen, Yusheng Liao, Shuyang Jiang +4

Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer he…