使用基于智能体AI自然语言框架的幻觉缓解方法
Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks
AI总结：
本研究通过设计多级智能体流水线，利用NLP框架和OVON接口传递上下文，引入300多个提示并采用KPI评估，有效缓解生成式AI模型中的幻觉问题，增强AI系统可信度。
摘要：
幻觉仍然是当前生成式AI模型中的重大挑战，削弱了人们对AI系统的信任及其可靠性。本研究探讨了如何通过编排多个专门的智能体来帮助缓解此类幻觉，重点关注利用自然语言处理（NLP）促进智能体之间无缝交互的系统。为实现这一目标，我们设计了一个流水线，向前端智能体引入超过三百个特意设计以诱发幻觉的提示。随后，输出由第二级和第三级智能体系统性地审查和修正，每个智能体采用不同的大型语言模型和定制策略来检测未经验证的声明、加入明确免责声明并澄清推测性内容。此外，我们引入了一组新颖的关键绩效指标（KPI），专门用于评估幻觉评分水平。一个专门的第四级AI智能体被用于评估这些KPI，提供详细评估并确保准确量化与幻觉相关行为的变化。本研究的核心组成部分是使用OVON（开放语音网络）框架，该框架依赖基于通用NLP的接口在智能体之间传递上下文信息。通过结构化的JSON消息，每个智能体传达其对幻觉可能性的评估以及可疑内容背后的原因，从而使后续阶段能够在保留上下文的情况下优化文本。结果表明，采用多个能够通过基于NLP的智能体框架相互操作的专门智能体，可以在幻觉缓解方面产生有前景的结果，最终增强AI社区内的信任。
英文摘要：
Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating multiple specialized Artificial Intelligent Agents can help mitigate such hallucinations, with a focus on systems leveraging Natural Language Processing (NLP) to facilitate seamless agent interactions. To achieve this, we design a pipeline that introduces over three hundred prompts, purposefully crafted to induce hallucinations, into a front-end agent. The outputs are then systematically reviewed and refined by second- and third-level agents, each employing distinct large language models and tailored strategies to detect unverified claims, incorporate explicit disclaimers, and clarify speculative content. Additionally, we introduce a set of novel Key Performance Indicators (KPIs) specifically designed to evaluate hallucination score levels. A dedicated fourth-level AI agent is employed to evaluate these KPIs, providing detailed assessments and ensuring accurate quantification of shifts in hallucination-related behaviors. A core component of this investigation is the use of the OVON (Open Voice Network) framework, which relies on universal NLP-based interfaces to transfer contextual information among agents. Through structured JSON messages, each agent communicates its assessment of the hallucination likelihood and the reasons underlying questionable content, thereby enabling the subsequent stage to refine the text without losing context. The results demonstrate that employing multiple specialized agents capable of interoperating with each other through NLP-based agentic frameworks can yield promising outcomes in hallucination mitigation, ultimately bolstering trust within the AI community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
