企业级AI助手通过多智能体互动澄清，提升模糊查询理解能力。
ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant
- 构建多智能体框架，动态生成澄清问题
- 在真实客户数据上，澄清问题质量显著优于传统方法
- 适合需要精准交互的企业对话系统研发者
大型语言模型在多种应用中展现出自然语言理解与生成的显著进展。然而，在涉及上下文和领域知识的企业级实际交互中，它们常难以解决歧义问题。本演示介绍ECLAIR（Enhanced CLArification for Interactive Responses），一种用于交互式消歧的多智能体框架。ECLAIR通过定义定制化智能体、进行歧义推理、生成澄清问题，并利用用户反馈优化最终响应，实现对模糊用户查询的增强澄清。在真实客户数据上的测试表明，ECLAIR在澄清问题生成方面显著优于标准少样本方法。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolving ambiguities in real-world, enterprise-level interactions, where context and domain-specific knowledge play a crucial role. In this demonstration, we introduce ECLAIR (Enhanced CLArification for Interactive Responses), a multi-agent framework for interactive disambiguation. ECLAIR enhances ambiguous user query clarification through an interactive process where custom agents are defined, ambiguity reasoning is conducted by the agents, clarification questions are generated, and user feedback is leveraged to refine the final response. When tested on real-world customer data, ECLAIR demonstrates significant improvements in clarification question generation compared to standard few-shot methods.
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