5 papers · 1 filter
Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science
Jingru Fan, Dewen Liu, Yufan Dang +15
Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range…
AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs
Xuanwen Ding, Chengjun Pan, Zejun Li +3
Evaluating multimodal large language models (MLLMs) is increasingly expensive, as the growing size and cross-modality complexity of benchmarks demand significant scoring efforts. T…
From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
Xinyi Mou, Xuanwen Ding, Qi He +8
Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements i…
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis
Xuanwen Ding, Jie Zhou, Liang Dou +4
Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on…
Recent Advances of Foundation Language Models-based Continual Learning: A Survey
Yutao Yang, Jie Zhou, Xuanwen Ding +5
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neu…