还能构建多个LLM的agent通过debate 策略提升数据生成的质量,优化SFT(例如Debatetune,DebateGPT),另外,有人研究了debate策略的拓扑结构,通过简单的debate拓扑结构能够减少token消耗,提升模型的精度(Group Debate),还有人研究了debate后达成一致的投票过程加了置信度(weighted),使用不同的LLM(ChatGPT, Bard, Claude...
Chateval:通过多智能体辩论实现更好的基于 LLM 的评估器 https://arxiv.org/abs/2308.:07201ABSTRACTLLM 可以代替人类进行文本作品评估评价,单一智能体评估和人类评估质量有差距。多智能体辩论的评估模式。构建…
python distributed_debate.py --role pro --pro-host localhost --pro-port 12011 --is-human 若您希望由另一用户担任反方，可以使用以下命令启动反方的Agent服务器，同样需要注意正确填写--con-host的值。若希望让大模型参与辩论，只需移除--is-human参数即可：python distributed_debate.py --role con --con...
本文探讨了多智能体辩论(MAD)框架在解决大型语言模型面临的思维退化(DOT)问题中的应用。DOT问题表现为模型一旦对某个解决方案产生信心，即使该方案可能错误，后续难以通过自我反思生成新思维。为克服此挑战，MAD框架被提出，它由多个智能体按顺序表达观点，一位裁判监督并给出最终答案。MAD框架旨在促进语言模...
Multi-Agent Debate:Multi-Agent Debate试图构建具有多代理对话的LLM应用程序,是鼓励LLM中发散思维的有效方式,并改善了LLM的事实性和推理。在这两种工作中 ,多个LLM推理实例被构建为多个Agent来解决与Agent争论的问题。每个Agent都是一个LLM推理实例,而不涉及任何工具或人员,并且Agent间的对话需要遵循预定义的顺序。
We introduce two approaches: a uniform prompt multi-agent debate and a diverse prompt multi-agent debate where each LLM agent adopts distinct roles such as fact-checker, journalist, or data scientist. These methods are benchmarked against single LLM evaluations to assess the impact of collaborative...
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs. We benchmark a range of debating and prompting strategies to explore the trade-offs between cost, time, and accuracy. Importantly, we find that multi-agent debating systems, ...
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs. We benchmark a range of debating and prompting strategies to explore the trade-offs between cost, time, and accuracy. Importantly, we find that multi-agent debating systems, ...
相关论文:《ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate》 文本评估历来面临重大挑战,经常需要大量的人力和时间成本。随着大型语言模型(LLMs)的出现,研究人员已探索将LLMs作为人类评估的替代品的可能性。虽然这些基于单一代理的方法显示出潜力,但实验结果表明,需要进一步的发展才能缩小它们...
python distributed_debate.py --role con --con-host localhost --con-port 12012 --is-human 上面启动的两个 Agent 服务器都会长期运行,如果想要关闭,可以使用 Ctrl + C,或是直接关闭对应的命令行窗口,但是请确保应用运行过程中这两个服务器进程都在正常工作。