# Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions

arXiv: https://arxiv.org/abs/2209.15565 · HTML: https://mlsanity.com/paper/2209.15565/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [Solving General Arithmetic Word Problems](https://mlsanity.com/paper/1608.01413/) (2016)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [A Survey on Semantic Parsing](https://mlsanity.com/paper/1812.00978/) (2018)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [AI Song Contest: Human-AI Co-Creation in Songwriting](https://mlsanity.com/paper/2010.05388/) (2020)
- [Text Generation with Text-Editing Models](https://mlsanity.com/paper/2206.07043/) (2022)
- [An End-to-End Dialogue Summarization System for Sales Calls](https://mlsanity.com/paper/2204.12951/) (2022)
- [Augmenting Poetry Composition with Verse by Verse](https://mlsanity.com/paper/2103.17205/) (2021)

## Built on by (most cited first, 30 of 54)

- [NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions](https://mlsanity.com/paper/2303.08233/) (2023)
- [OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models](https://mlsanity.com/paper/2402.10172/) (2024)
- [OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling](https://mlsanity.com/paper/2407.09887/) (2024)
- [Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling](https://mlsanity.com/paper/2505.11792/) (2025)
- [Synthetic Dialogue Dataset Generation using LLM Agents](https://mlsanity.com/paper/2401.17461/) (2024)
- [OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale](https://mlsanity.com/paper/2407.19633/) (2024)
- [Holy Grail 2.0: From Natural Language to Constraint Models](https://mlsanity.com/paper/2308.01589/) (2023)
- [StepORLM: A Self-Evolving Framework With Generative Process Supervision For Operations Research Language Models](https://mlsanity.com/paper/2509.22558/) (2025)
- [Solving General Natural-Language-Description Optimization Problems with Large Language Models](https://mlsanity.com/paper/2407.07924/) (2024)
- [From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto](https://mlsanity.com/paper/2402.16269/) (2024)
- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling](https://mlsanity.com/paper/2510.27610/) (2025)
- [Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems](https://mlsanity.com/paper/2212.13201/) (2022)
- [Synthesis of Mathematical programs from Natural Language Specifications](https://mlsanity.com/paper/2304.03287/) (2023)
- [LLMs for Cold-Start Cutting Plane Separator Configuration](https://mlsanity.com/paper/2412.12038/) (2024)
- [A Novel Approach for Auto-Formulation of Optimization Problems](https://mlsanity.com/paper/2302.04643/) (2023)
- [Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc](https://mlsanity.com/paper/2503.10642/) (2025)
- [ATG: Benchmarking Automated Theorem Generation for Generative Language Models](https://mlsanity.com/paper/2405.06677/) (2024)
- [Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description](https://mlsanity.com/paper/2212.03575/) (2022)
- [VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition](https://mlsanity.com/paper/2212.07219/) (2022)
- [Accelerating process control and optimization via machine learning: A review](https://mlsanity.com/paper/2412.18529/) (2024)
- [OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling](https://mlsanity.com/paper/2601.19924/) (2026)
- [Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentations](https://mlsanity.com/paper/2212.14657/) (2022)
- [OPD@NL4Opt: An ensemble approach for the NER task of the optimization problem](https://mlsanity.com/paper/2301.02459/) (2023)
- [Language Models for Business Optimisation with a Real World Case Study in Production Scheduling](https://mlsanity.com/paper/2309.13218/) (2023)
- [Abstract Operations Research Modeling Using Natural Language Inputs](https://mlsanity.com/paper/2408.07272/) (2024)
- [DualSchool: How Reliable are LLMs for Optimization Education?](https://mlsanity.com/paper/2505.21775/) (2025)
- [DCP-Bench-Open: Evaluating LLMs for Constraint Modelling of Discrete Combinatorial Problems](https://mlsanity.com/paper/2506.06052/) (2025)
- [Performance of LLMs on Stochastic Modeling Operations Research Problems: From Theory to Practice](https://mlsanity.com/paper/2506.23924/) (2025)
- [LLM-QUBO: An End-to-End Framework for Automated QUBO Transformation from Natural Language Problem Descriptions](https://mlsanity.com/paper/2509.00099/) (2025)

## Abstract (arXiv)

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.