mlsanityData to 7 Oct 2026

Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description

arXiv 2212.03575 ↗First posted 7 Dec 2022cs.CLcited by 5

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Abstract (arXiv)

In this report, I address auto-formulation of problem description, the task of converting an optimization problem into a canonical representation. I first simplify the auto-formulation task by defining an intermediate representation, then introduce entity tag embedding to utilize a given entity tag information. The ablation study demonstrate the effectiveness of the proposed method, which finally took second place in NeurIPS 2022 NL4Opt competition subtask 2.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 2 references that are in the map. First posted on arXiv 7 Dec 2022.

Builds on

2 of 2 references.

References · 2

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2022Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptionscited by 54

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 1.

5 papers.

  1. 2023NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptionscited by 133
  2. 2024From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifestocited by 11
  3. 2022Highlighting Named Entities in Input for Auto-Formulation of Optimization Problemscited by 8
  4. 2024Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Taskscited by 8
  5. 2025CP-Model-Zoo: A Natural Language Query System for Constraint Programming Modelscited by 0