Question Types for Knowledge Acquisition in LLM-based Dialogue Systems: Experiments with Simulated and Human Users

2026-07-20 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A study by Kazunori Komatani, Ryu Takeda, and Mikio Nakano, presented at SIGDIAL 2026, investigated the impact of question types on knowledge acquisition in LLM-based dialogue systems. The research compared implicit, explicit, and wh-questions using both an LLM-based user simulator and crowdsourced human participants. Findings revealed no substantial differences in user annoyance across the three question types. However, the effectiveness in eliciting correct responses varied significantly: wh-questions were less effective, while implicit and explicit questions performed comparably. This suggests that candidate-guided question forms (implicit or explicit) are more useful when the system has a plausible candidate answer. Conversely, wh-questions may be more appropriate when the system lacks sufficient confidence to ask a more specific question, despite their lower efficacy in eliciting correct information.

Key takeaway

For NLP Engineers designing LLM-based dialogue systems focused on knowledge acquisition, prioritize candidate-guided question forms. If your system has a plausible answer candidate, implicit or explicit questions will more effectively elicit correct user responses. Reserve wh-questions for scenarios where your system lacks confidence, understanding they may yield less precise information. This strategy optimizes data collection without increasing user annoyance.

Key insights

Implicit and explicit questions are more effective for knowledge acquisition in LLM dialogues than wh-questions.

Principles

• Question formulation impacts information obtained.
• User annoyance is not significantly affected by question type.

Method

The study compared implicit, explicit, and wh-questions using an LLM-based user simulator and human participants to assess knowledge acquisition and user annoyance.

In practice

• Use candidate-guided questions when a plausible answer exists.
• Employ wh-questions when system confidence is low.

Topics

• LLM-based Dialogue Systems
• Knowledge Acquisition
• Question Types
• User Simulation
• Human-Computer Interaction
• Natural Language Processing

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.