Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA
arXiv:2605.25204v1 Announce Type: new Abstract: Pluralistic alignment requires systems to adapt to diverse user values, communication styles, and contextual assumptions. We believe that a foundational prerequisite for such alignment enabling accurate preference elicitation from people when their intent is under-specified or ambiguous. We study the problem of preference elicitation in multi-turn question answering by decomposing the problem into two components: a \textbf{clarification policy}, which decides whether to ask a clarifying question or answer directly, and \textbf{post-clarification
This research addresses a critical bottleneck in AI user interaction as AI systems become more prevalent and responsible for complex tasks.
Improved multi-turn question answering and preference elicitation are essential for building more reliable and user-aligned AI agents beyond simple chatbots.
The focus shifts from merely clarifying intent to effectively providing answers post-clarification, highlighting a deeper challenge in AI's nuanced understanding and response capabilities.
- · AI research labs
- · AI software developers
- · Companies building advanced conversational AI
- · AI systems with poor multi-turn QA
- · Users frustrated by ineffective AI interactions
More robust and reliable AI systems capable of handling complex user inquiries will emerge.
This will accelerate the deployment of AI agents in critical professional and consumer applications.
Enhanced AI understanding and response could lead to higher user trust and broader societal acceptance of autonomous AI systems.
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Read at arXiv cs.CL