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Feedback-integrated prompt optimiser for problem formulation.

Pivithuru Thejan Amarasinghe1, Su Nguyen2, Yuan Sun3

  • 1Research Center for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia. p.amarasinghe@latrobe.edu.au.

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|November 25, 2025
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Summary
This summary is machine-generated.

We introduce FIPO, a method to improve automated optimization problem formulation using Small Language Models (SLMs) with feedback-guided prompt optimization. This makes complex modeling more accessible and efficient.

Keywords:
Language modelsProblem formulationPrompt optimisation

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computational Optimization

Background:

  • Problem formulation in optimization modeling is complex and requires expert knowledge.
  • Automating this process is challenging due to the need to replicate human reasoning.
  • Large Language Models (LLMs) show promise but are computationally expensive; Small Language Models (SLMs) are efficient but less capable.

Purpose of the Study:

  • To enhance the problem formulation capabilities of SLMs for optimization modeling.
  • To develop a resource-efficient method for automated problem formulation.
  • To address the limitations of SLMs in complex reasoning tasks.

Main Methods:

  • Proposed FIPO (Feedback-Integrated Prompt Optimiser) for iterative, feedback-driven prompt optimization.
  • Integrated a local search algorithm with structured feedback from simulated expert evaluations.
  • Evaluated FIPO on the LPWP dataset for automated problem formulation.

Main Results:

  • FIPO demonstrated consistent performance gains over existing state-of-the-art prompt optimization methods.
  • Showcased the effectiveness of feedback-guided prompt evolution for SLMs.
  • Achieved improved accuracy and scalability in automated problem formulation.

Conclusions:

  • Feedback-guided prompt evolution is a viable strategy for enhancing SLM performance in automated problem formulation.
  • FIPO offers a cost-efficient and scalable solution for complex optimization tasks.
  • This approach makes advanced optimization modeling more accessible.