Adhering to the workflow of LLM-based multi-agent systems, we synthesize a general structure encompassing five key components: profile, perception, self-action, mutual interaction, and evolution. This unified framework encapsulates much of the previous work in the field. Furthermore, we illuminate ...
Multi-expert Prompting is the first to tackle the challenge of aggregating multi-agent long-form responses in a single turn based on well-studied perspectives from management sciences. It sig-nificantly outperforms baselines in improving the truthfulness, factuality, toxicity, hurtfulness, infor-mati...
Repository to deploy LLMs with Multi-GPUs in distributed Kubernetes nodes - multi-gpu-llms/test-notebooks/README.md at 7ddc3f7c1b03dcc2fe9b3f9a81971e043337f3d9 · rh-aiservices-bu/multi-gpu-llms
AgentVerse offers a versatile framework that streamlines the process of creating custom multi-agent environments for large language models (LLMs). Designed to facilitate swift development and customization with minimal effort, our framework empowers researchers to concentrate on their research, rather ...
The integration of experimental technologies with large language models (LLMs) is transforming scientific research, positioning AI as a versatile research assistant rather than a mere problem-solving tool. In the field of power systems, however, managing simulations -- one of the essential experimental...
Agent Prune: A Robust and Economic Multi-Agent Communication Framework for LLMs that Saves Cost and Removes Redundant and Malicious Contents
最近在研究AI智能体相关技术如何应用到具身智能机器人上,这里分享一篇多大型语言模型(LLM)代理的研究论文,标题为“Small LLMs Are Weak Tool Learners: A Multi-LLM Agent”。 1. **研究背景**: - 大型语言模…
Large Language Model Agent for Fake News Detection[paper] Argumentative Large Language Models for Explainable and Contestable Decision-Making[paper] RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information[paper] RAGAR, Your...
🌐Custom Domain: If users have their own domain, they can bind it to the platform for quick access to the dialogue agent from anywhere. 🔒Privacy Protection: All data is stored locally in the user's browser, ensuring user privacy. ...
Recent advancements in large language models (LLMs) underscore their potential for responding to inquiries in various domains. However, ensuring that generative agents provide accurate and reliable answers remains an ongoing challenge. In this context, multi-agent debate (MAD) has emerged as a ...