most citedRGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

3 citations · 3 across the 3 of their papers we have counts for

collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2025

Slow Tuning and Low-Entropy Masking for Safe Chain-of-Thought Distillation

Ziyang Ma, Qingyue Yuan, Linhai Zhang +1

Previous chain-of-thought (CoT) distillation methods primarily focused on enhancing the reasoning capabilities of Small Language Models (SLMs) by utilizing high-quality rationales…

cs.CL2025

Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

Linhai Zhang, Ziyang Gao, Deyu Zhou +1

Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce professionals highlights the need…

cs.CL2025

PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation

Linhai Zhang, Jialong Wu, Deyu Zhou +1

Personalized large language models (LLMs) aim to tailor their outputs to user preferences. Recent advances in parameter-efficient fine-tuning (PEFT) methods have highlighted the ef…

cs.CL2025

SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling

Xin Zhang, Qiyu Wei, Yingjie Zhu +3

User reviews on e-commerce platforms exhibit dynamic sentiment patterns driven by temporal and contextual factors. Traditional sentiment analysis methods focus on static reviews, f…

cs.CL2025

Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

Yanyue Zhang, Yulan He, Deyu Zhou

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models…

cs.CL20253 cited

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

Sichu Liang, Linhai Zhang, Hongyu Zhu +3

Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical kno…