Summary of Agent-cq: Automatic Generation and Evaluation Of Clarifying Questions For Conversational Search with Llms, by Clemencia Siro et al.
AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs
by Clemencia Siro, Yifei Yuan, Mohammad Aliannejadi, Maarten de Rijke
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes AGENT-CQ, an end-to-end framework for generating diverse and effective clarifying questions in open-domain conversational search systems. The framework consists of two stages: a generation stage that employs LLM prompting strategies, and an evaluation stage (CrowdLLM) that assesses generated questions and answers based on quality metrics. Experiments on the ClariQ dataset show that CrowdLLM effectively evaluates question and answer quality, while human evaluation and CrowdLLM demonstrate that AGENT-CQ’s generation stage outperforms baselines in various aspects of question and answer quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to ask questions that helps improve how well search systems can understand what you’re looking for. The new method, called AGENT-CQ, uses special AI models to come up with good questions that help find the right answers. It has two parts: one that makes the questions and another that checks if they’re good or not. The results show that this approach works better than other methods and helps search systems do a better job of finding what you need. |
Keywords
» Artificial intelligence » Prompting
