Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
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TL;DR - OR-Clarify benchmarks whether LLM agents detect and resolve missing information before formulating optimization problems. Its InterOPT framework improves exact recovery of hidden specification details by selectively asking questions and stopping when the formulation is sufficiently complete.
- OR-Clarify withholds objectives, constraints, or business rules and evaluates agents through bounded interaction with a simulated user.
- Metrics cover slot recovery, stopping behavior, silent assumptions, and interaction cost.
- InterOPT first identifies formulation-critical gaps, then decides whether to ask another question or stop.
- InterOPT substantially outperforms baselines in exact slot recovery for choice-based clarification and remains competitive in open-ended tests.
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Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
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Hugging Face upvotes 19
TL;DR - OR-Clarify benchmarks whether LLM agents detect and resolve missing information before formulating optimization problems. Its InterOPT framework improves exact recovery of hidden specification details by selectively asking questions and stopping when the formulation is sufficiently complete.
- OR-Clarify withholds objectives, constraints, or business rules and evaluates agents through bounded interaction with a simulated user.
- Metrics cover slot recovery, stopping behavior, silent assumptions, and interaction cost.
- InterOPT first identifies formulation-critical gaps, then decides whether to ask another question or stop.
- InterOPT substantially outperforms baselines in exact slot recovery for choice-based clarification and remains competitive in open-ended tests.