1 citations · 5 across the 22 of their papers we have counts for
12 papers · 1 filter
Concept than Document: Context Compression via AMR-based Conceptual Entropy
Kaize Shi, Xueyao Sun, Xiaohui Tao +3
Large Language Models (LLMs) face information overload when handling long contexts, particularly in Retrieval-Augmented Generation (RAG) where extensive supporting documents often…
Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint
Yanrui Du, Fenglei Fan, Sendong Zhao +6
Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance various abilities of Large Language Models (LLMs). However, prior studies ha…
Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning
Qika Lin, Yifan Zhu, Bin Pu +14
The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale…
Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection
Yiming Xu, Jiarun Chen, Zhen Peng +5
The natural combination of intricate topological structures and rich textual information in text-attributed graphs (TAGs) opens up a novel perspective for graph anomaly detection (…
Synergistic Prompting for Robust Visual Recognition with Missing Modalities
Zhihui Zhang, Luanyuan Dai, Qika Lin +5
Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, i…
Endoscopic Depth Estimation Based on Deep Learning: A Survey
Ke Niu, Zeyun Liu, Xue Feng +5
Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers…
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Jingying Ma, Feng Wu, Qika Lin +4
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to a…
Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis
Luwei Xiao, Rui Mao, Shuai Zhao +4
Multimodal aspect-based sentiment classification (MASC) is an emerging task due to an increase in user-generated multimodal content on social platforms, aimed at predicting sentime…
HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
Haoran Luo, Haihong E, Guanting Chen +9
Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing gr…
A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models
Qika Lin, Zhen Peng, Kaize Shi +5
Recent years have witnessed rapid advances in graph representation learning, with the continuous embedding approach emerging as the dominant paradigm. However, such methods encount…
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
Qika Lin, Tianzhe Zhao, Kai He +5
Due to the presence of the natural gap between Knowledge Graph (KG) structures and the natural language, the effective integration of holistic structural information of KGs with La…
KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search
Haoran Luo, Haihong E, Yikai Guo +7
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language model…