ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant
This addresses the issue of ambiguous queries in enterprise AI assistants, though it appears incremental as it builds on existing multi-agent and clarification methods.
The paper tackles the problem of LLMs struggling with ambiguities in enterprise interactions by introducing ECLAIR, a multi-agent framework for interactive disambiguation, which shows significant improvements in clarification question generation on real-world customer data.
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.