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English(EN) Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution

新AI系统PAWNI优化LLM的提示词制定

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了PAWNI（Prompt Architecture Wizard using Neural Intelligence），一个多智能体对话系统，旨在改进大型语言模型（LLMs）的提示词制定。PAWNI利用八个智能体通过对话引导用户，将非结构化查询转化为结构化提示词，并优化问题本身而非模型的响应。一项对四名参与者的探索性研究表明，PAWNI显著提高了提示词的结构完整性，改善了LLM的输出质量，并减少了用户的工作量，使得一次交互即可获得满意的结果。 AI

影响 该系统可以显著提高人与LLM交互的效率和有效性，减少无效的交互轮次并提高输出质量。

排序理由 该集群描述了一篇详细介绍提示词制定新系统的研究论文。

在 arXiv cs.MA (Multiagent) 阅读 →

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新AI系统PAWNI优化LLM的提示词制定

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · B. Sankar, Pawni Yadav, Srinidhi Ranjini Girish, Amogh A. S ·

    正确提问的方式：用于复杂任务解决中提示词制定的多智能体对话系统

    arXiv:2608.01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing co…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Amogh A. S ·

    问对问题：用于复杂任务解决中提示词制定的多智能体对话系统

    Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing cognitive returns. We present PAWNI (Prompt Architecture Wiz…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Amogh A. S ·

    问对问题：用于复杂任务解决中提示词制定的多智能体对话系统

    Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing cognitive returns. We present PAWNI (Prompt Architecture Wiz…

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