mlsanityData to 14 Oct 2026

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

arXiv 2502.09994 ↗First posted 14 Feb 2025cs.AIcited by 9

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Abstract (arXiv)

Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to produce meaningful explanations. This lack of clarity raises concerns about transparency and trustworthiness in OR applications. To address these challenges, we propose a comprehensive framework, Explainable Operations Research (EOR), emphasizing actionable and understandable explanations accompanying optimization. The core of EOR is the concept of Decision Information, which emerges from what-if analysis and focuses on evaluating the impact of complex constraints (or parameters) changes on decision-making. Specifically, we utilize bipartite graphs to quantify the changes in the OR model and adopt LLMs to improve the explanation capabilities. Additionally, we introduce the first industrial benchmark to rigorously evaluate the effectiveness of explanations and analyses in OR, establishing a new standard for transparency and clarity in the field.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 14 Feb 2025.

Builds on

14 of 32 references.

References · 14

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
  3. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 318
  4. 2023NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptionscited by 135
  5. 2024OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Modelscited by 109
  6. 2023Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Studycited by 85
  7. 2023Large Language Models for Supply Chain Optimizationcited by 64
  8. 2024Are LLM-based Evaluators Confusing NLG Quality Criteria?cited by 36
  9. 2024When Large Language Model Meets Optimizationcited by 22
  10. 2024Solving General Natural-Language-Description Optimization Problems with Large Language Modelscited by 22
  11. 2024LLMs for XAI: Future Directions for Explaining Explanationscited by 19
  12. 2022Cardinal Optimizer (COPT) User Guidecited by 16
2 more
  1. 2024Explainability through uncertainty: Trustworthy decision-making with neural networkscited by 12
  2. 2024A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimizationcited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 3.

9 papers.

  1. 2025A Survey of Optimization Modeling Meets LLMs: Progress and Future Directionscited by 31
  2. 2025SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modelingcited by 8
  3. 2025Large Language Models in Operations Research: Methods, Applications, and Challengescited by 6
  4. 2025DualSchool: How Reliable are LLMs for Optimization Education?cited by 4
  5. 2026Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimizationcited by 3
  6. 2025Teaching LLMs to Think Mathematically: A Critical Study of Decision-Making via Optimizationcited by 1
  7. 2025Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representationcited by 1
  8. 2026Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Modelscited by 1
  9. 2026WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Imagescited by 1