mlsanityData to 7 Oct 2026

NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions

arXiv 2303.08233 ↗First posted 14 Mar 2023cs.CL cs.AIcited by 133

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Cited by 25 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description. Specifically, the goal of the competition is to increase the accessibility and usability of optimization solvers by allowing non-experts to interface with them using natural language. We separate this challenging goal into two sub-tasks: (1) recognize and label the semantic entities that correspond to the components of the optimization problem; (2) generate a meaning representation (i.e., a logical form) of the problem from its detected problem entities. The first task aims to reduce ambiguity by detecting and tagging the entities of the optimization problems. The second task creates an intermediate representation of the linear programming (LP) problem that is converted into a format that can be used by commercial solvers. In this report, we present the LP word problem dataset and shared tasks for the NeurIPS 2022 competition. Furthermore, we investigate and compare the performance of the ChatGPT large language model against the winning solutions. Through this competition, we hope to bring interest towards the development of novel machine learning applications and datasets for optimization modeling.

Key facts. Cited by 133 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

21 of 40 references.

References · 21

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2018Deep contextualized word representationscited by 2,285
  7. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
  8. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  9. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  10. 2018Graph Convolutional Networks for Text Classificationcited by 345
  11. 2021A Diverse Corpus for Evaluating and Developing English Math Word Problem Solverscited by 314
  12. 2020Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlookscited by 312
9 more
  1. 2016Language to Logical Form with Neural Attentioncited by 158
  2. 2018NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating Systemcited by 97
  3. 2022Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptionscited by 54
  4. 2022Highlighting Named Entities in Input for Auto-Formulation of Optimization Problemscited by 8
  5. 2023A Novel Approach for Auto-Formulation of Optimization Problemscited by 7
  6. 2022Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Descriptioncited by 5
  7. 2022VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognitioncited by 5
  8. 2022Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentationscited by 4
  9. 2023OPD@NL4Opt: An ensemble approach for the NER task of the optimization problemcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 15; 2025: 46; 2026: 70.

133 papers.

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  1. 2024OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Modelscited by 105
  2. 2024LLMOPT: Learning to Define and Solve General Optimization Problems from Scratchcited by 102
  3. 2024OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modelingcited by 66
  4. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 65
  5. 2024Autoformulation of Mathematical Optimization Models Using LLMscited by 48
  6. 2025OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modelingcited by 48
  7. 2024LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languagescited by 46
  8. 2025Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modelingcited by 44
  9. 2024Synthetic Dialogue Dataset Generation using LLM Agentscited by 39
  10. 2024OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scalecited by 37
  11. 2025A Survey of Optimization Modeling Meets LLMs: Progress and Future Directionscited by 30
  12. 2025CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimizationcited by 28
38 more
  1. 2025StepORLM: A Self-Evolving Framework With Generative Process Supervision For Operations Research Language Modelscited by 26
  2. 2023Holy Grail 2.0: From Natural Language to Constraint Modelscited by 24
  3. 2024Solving General Natural-Language-Description Optimization Problems with Large Language Modelscited by 22
  4. 2025OptimAI: Optimization from Natural Language Using LLM-Powered AI Agentscited by 22
  5. 2025HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimizationcited by 19
  6. 2024LM4OPT: Unveiling the Potential of Large Language Models in Formulating Mathematical Optimization Problemscited by 18
  7. 2025OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learningcited by 16
  8. 2025Auto-Formulating Dynamic Programming Problems with Large Language Modelscited by 15
  9. 2026Automated Optimization Modeling via a Localizable Error-Driven Perspectivecited by 14
  10. 2025AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Librarycited by 12
  11. 2024Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Processcited by 11
  12. 2024From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifestocited by 11
  13. 2026Constructing Industrial-Scale Optimization Modeling Benchmarkcited by 10
  14. 2025Decision Information Meets Large Language Models: The Future of Explainable Operations Researchcited by 9
  15. 2025EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulationscited by 9
  16. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 9
  17. 2025OptiMind: Teaching LLMs to Think Like Optimization Expertscited by 9
  18. 2024Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Taskscited by 8
  19. 2025OptiChat: Bridging Optimization Models and Practitioners with Large Language Modelscited by 8
  20. 2025EvoCut: Strengthening Integer Programs via Evolution-Guided Language Modelscited by 8
  21. 2025SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modelingcited by 8
  22. 2026ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimizationcited by 8
  23. 2025LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Mergingcited by 7
  24. 2025Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinccited by 7
  25. 2025ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modelingcited by 7
  26. 2024GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generationcited by 6
  27. 2025Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systemscited by 6
  28. 2025CP-Agent: Agentic Constraint Programmingcited by 6
  29. 2025R-ConstraintBench: Evaluating LLMs on NP-Complete Schedulingcited by 5
  30. 2026OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scalingcited by 5
  31. 2026NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agentscited by 5
  32. 2026Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verificationcited by 5
  33. 2023Language Models for Business Optimisation with a Real World Case Study in Production Schedulingcited by 4
  34. 2025Large Language Models in Operations Research: Methods, Applications, and Challengescited by 4
  35. 2026OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agentscited by 4
  36. 2026FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimizationcited by 4
  37. 2025CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Codecited by 3
  38. 2025DualSchool: How Reliable are LLMs for Optimization Education?cited by 3

50 most cited of 133 All 133 (JSON)