2.1k citations · 2.1k across the 19 of their papers we have counts for
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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…
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…
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…
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…