5 citations · 13 across the 19 of their papers we have counts for
15 papers · 1 filter
EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading
Zhihao Wu, Linhai Zhang, Taiyi Wang +4
Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student answer. Existing credit-assig…
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…
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…
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…
CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
Zhenyi Shen, Hanqi Yan, Linhai Zhang +3
Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by encouraging step-by-step reasoning in natural language. However, leveraging a latent continuous space for…
SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation
Jialong Wu, Zhenglin Wang, Linhai Zhang +3
Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ig…
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…
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
Zeng Yang, Linhai Zhang, Deyu Zhou
Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets i…