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ECLAIR: Enhanced Clarification for Interactive Responses

arXiv 2503.15739 ↗First posted 19 Mar 2025cs.AIcited by 2

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Abstract (arXiv)

We present ECLAIR (Enhanced CLArification for Interactive Responses), a novel unified and end-to-end framework for interactive disambiguation in enterprise AI assistants. ECLAIR generates clarification questions for ambiguous user queries and resolves ambiguity based on the user's response.We introduce a generalized architecture capable of integrating ambiguity information from multiple downstream agents, enhancing context-awareness in resolving ambiguities and allowing enterprise specific definition of agents. We further define agents within our system that provide domain-specific grounding information. We conduct experiments comparing ECLAIR to few-shot prompting techniques and demonstrate ECLAIR's superior performance in clarification question generation and ambiguity resolution.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 19 Mar 2025.

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4 of 10 references.

References · 4

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  3. 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaborationcited by 116
  4. 2022PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Financecited by 45

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Citing papers per year: 2026: 2.

2 papers.