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New benchmark evaluates LLMs' ability to clarify incomplete optimization requests

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced OR-Clarify, a new benchmark designed to evaluate how well large language models (LLMs) can identify and request clarification for incomplete information when formulating optimization models from natural language. The benchmark assesses agents on their ability to recover missing objectives, constraints, or business rules before proceeding. Alongside this, they propose InterOPT, a framework that guides LLMs on when to ask clarifying questions and when to stop, demonstrating improved performance in slot recovery compared to existing methods. AI

IMPACT This research could lead to more robust AI assistants for complex problem-solving, improving the accuracy of optimization models derived from natural language.

RANK_REASON The cluster contains an academic paper detailing a new benchmark and framework for LLM interaction in optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark evaluates LLMs' ability to clarify incomplete optimization requests

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The cluster contains an academic paper detailing a new benchmark and framework for LLM interaction in optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sihan Ge, Yichen Lin, Chenyu Zhou, Jianghao Lin, Tao Yao, Dongdong Ge ·

    Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

    arXiv:2609.05258v1 Announce Type: cross Abstract: 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,…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

    OR-Clarify benchmarks clarification before optimization modeling, and InterOPT guides agents to ask questions or stop based on missing formulation-critical details.

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