Fu & Du 2025 — First Ask Then Answer (FATA)
Core Contribution
Proposes a two-stage interaction paradigm where LLMs proactively generate supplementary questions before answering, rather than reactively clarifying only when ambiguity is detected.
Key Insight
The paper identifies an expertise-information gap: AI systems possess extensive knowledge capabilities, but non-expert users lack the domain frameworks necessary to formulate comprehensive queries. Users can’t anticipate what information experts would consider essential.
The solution: leverage AI to externalize expert consultation patterns, transforming implicit professional knowledge-gathering into explicit, accessible questioning frameworks.
Framework Components
Stage 1 (F1): Systematic question generation across five dimensions:
- Contextual background
- Constraints (resource, time, regulatory)
- Preferences (goals, priorities, trade-offs)
- Environmental factors
- Historical context
Stage 2 (F2): Context-enhanced response generation integrating original query with collected supplementary information.
Key Design Decisions
- Single-turn question generation: all clarifying questions produced at once rather than multi-turn back-and-forth
- Proactive rather than reactive: doesn’t wait for detected ambiguity
- Prompt-only approach: no fine-tuning required, compatible with existing LLMs
- Built-in constraints: prevents over-questioning, protects privacy
Experimental Results
- 27.7–47.4% improvement over baseline prompts
- 2.1–5.4% improvement over expert-level prompts with complete context
- Stability rates improved from 11–22% to 77.8–100%
- Tested across 12 industry domains, 300 test cases
Limitations Acknowledged
- Simulated user responses (not actual humans)
- Single-turn may miss complex interdependencies
- Dependent on underlying model quality
- User comprehension of questions varies
Extractions for heyMHK
- 01-atom—expertise-information-gap
- 05-atom—proactive-vs-reactive-clarification
- 01-atom—information-scaffolding
- 05-molecule—fata-framework
- 07-molecule—expert-consultation-externalization
Related Work Referenced
- CLAM (Kuhn et al. 2022) - selective clarification
- Self-Ask (Press et al. 2022) - model self-questioning
- Chain-of-Thought prompting
- RAG limitations