Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
arXiv:2607. 01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation.
arXiv:2609.37588v1 Announce Type: new Abstract: Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the...
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --...
arXiv:2606. 30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified.
The paper introduces PlanPool, a method for agentic Text-to-SQL systems to manage clarification questions by maintaining a mutable question pool. By requiring agents to explicitly ask or drop each planned question, PlanPool improves ambiguity coverage and reduces silent failures compared to unconstrained or prompt-based approaches. Experiments on benchmarks derived from BIRD-Interact and Spider show that PlanPool achieves competitive execution accuracy while better handling underspecification.
Conversational agents can retrieve information directly or delegate retrieval to a subagent. This design choice affects how retrieval history is maintained, what context each model call receives, and...