5 citations · 8 across the 13 of their papers we have counts for
13 papers
MAP: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion
Dingyi Zhang, Ziqing Zhuang, Linhai Zhang +2
Persuasive dialogue generation plays a vital role in decision-making, negotiation, counseling, and behavior change, yet it remains a challenging problem. In complex persuasion wher…
Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning
Ziqing Zhuang, Linhai Zhang, Jiasheng Si +2
Large language models (LLMs) have demonstrated strong reasoning capabilities, and as existing approaches for enhancing LLM reasoning continue to mature, increasing attention has sh…
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…
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…
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…
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…
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…
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…
Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity
Xin Zhang, Linhai Zhang, Deyu Zhou +1
Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user d…
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment
Congzhi Zhang, Linhai Zhang, Deyu Zhou
Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased data…
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
Congzhi Zhang, Linhai Zhang, Jialong Wu +2
Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges relate…
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
Jialong Wu, Linhai Zhang, Deyu Zhou +1
Though notable progress has been made, neural-based aspect-based sentiment analysis (ABSA) models are prone to learn spurious correlations from annotation biases, resulting in poor…
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
Linhai Zhang, Jialong Wu, Deyu Zhou +1
Though Large Language Models (LLMs) have demonstrated the powerful capabilities of few-shot learning through prompting methods, supervised training is still necessary for complex r…