Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
Researchers release OR-Clarify, a benchmark testing whether LLM agents ask clarifying questions before formulating optimization models from incomplete requests.
OR-Clarify evaluates pre-formulation clarification in operations research: each task gives a partial problem description, withholds structured hidden slots, and scores agents via bounded interaction with a simulated user, measuring slot recovery, stopping behavior, silent assumptions, and interaction cost. The authors also propose InterOPT, a two-stage framework that identifies formulation-critical gaps to decide when to ask or stop. In choice-based experiments InterOPT substantially outperforms all baselines in exact slot recovery and remains competitive in the open-ended setting.
- Benchmark targets clarification before optimization modeling
- InterOPT two-stage framework decides when to ask vs stop
- Outperforms baselines in exact slot recovery (choice-based setting)
- Measures silent assumptions and interaction cost
- Products
- OR-ClarifyInterOPT
From the original article
1 min read at huggingface.co
Large language models (LLMs) are increasingly used to formulate optimization models from natural-language problem descriptions, yet realistic operations research (OR) requests are often incomplete: missing objectives, constraints, or business rules can change the resulting mathematical program.
By Sihan Ge, Yichen Lin, Chenyu Zhou · huggingface.co
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