REVIEW 4 cited by
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains.
Forward citations
Cited by 4 Pith papers
-
PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language
Training an LLM as a multi-turn agent that runs and repairs solver code raises verified optimization solve rates, with the 4B PEARL model outperforming DeepSeek-V3.2-685B in aggregate.
-
Learn2Zinc: Fine-tuning Small Language Models for Text-to-Model Translation in MiniZinc
Fine-tuning small LMs on synthetic and bootstrapped syntax-error corrections lifts MiniZinc execution accuracy from ~0% to 98% in an ensemble, but solution accuracy saturates near 35%.
-
DualSchool: How Reliable are LLMs for Optimization Education?
DualSchool shows that open LLMs explain dualization well but achieve at most 47.8% accuracy on generating correct duals, and fail at verification and error classification.
-
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
A literature survey that classifies LLM-based optimization research into modeling and solving, with solving divided into LLMs as optimizers, low-level components, and high-level managers.
Discussion (0). Continue with ORCID to comment.