97 citations · 129 across the 41 of their papers we have counts for
12 papers · 1 filter
Uncovering the Role of Initial Saliency in U-Shaped Attention Bias: Scaling Initial Token Weight for Enhanced Long-Text Processing
Zewen Qiang, Sendong Zhao, Haochun Wang +2
Large language models (LLMs) have demonstrated strong performance on a variety of natural language processing (NLP) tasks. However, they often struggle with long-text sequences due…
META-RAG: Meta-Analysis-Inspired Evidence-Re-Ranking Method for Retrieval-Augmented Generation in Evidence-Based Medicine
Mengzhou Sun, Sendong Zhao, Jianyu Chen +2
Evidence-based medicine (EBM) holds a crucial role in clinical application. Given suitable medical articles, doctors effectively reduce the incidence of misdiagnoses. Researchers f…
Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration
Jingbo Wang, Sendong Zhao, Haochun Wang +2
The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique experti…
PICOs-RAG: PICO-supported Query Rewriting for Retrieval-Augmented Generation in Evidence-Based Medicine
Mengzhou Sun, Sendong Zhao, Jianyu Chen +1
Evidence-based medicine (EBM) research has always been of paramount importance. It is important to find appropriate medical theoretical support for the needs from physicians or pat…
M-Eval: A Heterogeneity-Based Framework for Multi-evidence Validation in Medical RAG Systems
Mengzhou Sun, Sendong Zhao, Jianyu Chen +2
Retrieval-augmented Generation (RAG) has demonstrated potential in enhancing medical question-answering systems through the integration of large language models (LLMs) with externa…
QAgent: A modular Search Agent with Interactive Query Understanding
Yi Jiang, Lei Shen, Lujie Niu +3
Large language models (LLMs) excel at natural language tasks but are limited by their static parametric knowledge, especially in knowledge-intensive task. Retrieval-augmented gener…
MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security
Yanrui Du, Fenglei Fan, Sendong Zhao +3
As Large Language Models (LLMs) increasingly permeate human life, their security has emerged as a critical concern, particularly their ability to maintain harmless responses to mal…
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…
CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy
Yi Jiang, Sendong Zhao, Jianbo Li +4
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs), especially for knowledge-intensive tasks. Despite its advantages, current RAG methods often struggle to…
GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis
Yi Jiang, Sendong Zhao, Jianbo Li +2
The Retrieval-Augmented Generation (RAG) framework introduces a retrieval module to dynamically inject retrieved information into the input context of large language models (LLMs),…
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems
Haochun Wang, Sendong Zhao, Jingbo Wang +3
Multi-agent collaboration has emerged as a pivotal paradigm for addressing complex, distributed tasks in large language model (LLM)-driven applications. While prior research has fo…
ReLearn: Unlearning via Learning for Large Language Models
Haoming Xu, Ningyuan Zhao, Liming Yang +7
Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities. However, this paradigm disrupts the subsequent token…