1 citations · 1 across the 6 of their papers we have counts for
6 papers
Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection
Yutong Wang, Xuebo Liu, Derek F. Wong +5
Document-level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that impede global cohesion, while…
MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
Zhexuan Wang, Xuebo Liu, Li Wang +4
Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orchestrated via role-specific pro…
MASRubric: Auditing Information Flow in Multi-Agent Systems with Failure-Distilled Pitfall Rubrics
Yutong Wang, Siyuan Xiong, Xuebo Liu +4
While multi-agent systems (MAS) excel at complex reasoning, they are vulnerable to errors that intermediate agents introduce and downstream agents build upon. Auditing intermediate…
AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration
Chunhao Tian, Yutong Wang, Xuebo Liu +4
Proper initialization is crucial for any system, particularly in multi-agent systems (MAS), where it plays a pivotal role in determining both the system's efficiency and effectiven…
DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory
Yutong Wang, Jiali Zeng, Xuebo Liu +4
Large language models (LLMs) have achieved reasonable quality improvements in machine translation (MT). However, most current research on MT-LLMs still faces significant challenges…
TasTe: Teaching Large Language Models to Translate through Self-Reflection
Yutong Wang, Jiali Zeng, Xuebo Liu +3
Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. Techniques like instruction tuning have effectively enhanced the pr…