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Large Language Models for the Automated Analysis of Optimization Algorithms

Camilo Chacón Sartori, Christian Blum, Gabriela Ochoa

TL;DR

This work addresses the barrier of interpreting STN visualizations in optimization by integrating Large Language Models (LLMs) into STNWeb to generate natural language explanations and basic plots. The authors present a two-stage pipeline: prompt templates (A, B, C) and automatic feature extraction to produce tailored interpretations, leveraging off-the-shelf LLMs such as GPT-4. They demonstrate through an extensive evaluation that GPT-4-turbo excels at tasks requiring winner determination and parameter suggestions, while Task C plots can be generated via Chat2VIS, collectively enhancing explainability and accessibility across discrete and continuous optimization problems. The approach suggests broad applicability to other optimization tools, highlighting potential for improved adoption and trust in automated analysis of optimization algorithms.

Abstract

The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizations produced by STNWeb can be very informative for algorithm designers, they often require a certain level of prior knowledge to be interpreted. In an attempt to bridge this knowledge gap, we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive written reports, complemented by automatically generated plots, thereby enhancing the user experience and reducing the barriers to the adoption of this tool by the research community. Moreover, our approach can be expanded to other tools from the optimization community, showcasing the versatility and potential of LLMs in this field.

Large Language Models for the Automated Analysis of Optimization Algorithms

TL;DR

This work addresses the barrier of interpreting STN visualizations in optimization by integrating Large Language Models (LLMs) into STNWeb to generate natural language explanations and basic plots. The authors present a two-stage pipeline: prompt templates (A, B, C) and automatic feature extraction to produce tailored interpretations, leveraging off-the-shelf LLMs such as GPT-4. They demonstrate through an extensive evaluation that GPT-4-turbo excels at tasks requiring winner determination and parameter suggestions, while Task C plots can be generated via Chat2VIS, collectively enhancing explainability and accessibility across discrete and continuous optimization problems. The approach suggests broad applicability to other optimization tools, highlighting potential for improved adoption and trust in automated analysis of optimization algorithms.

Abstract

The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizations produced by STNWeb can be very informative for algorithm designers, they often require a certain level of prior knowledge to be interpreted. In an attempt to bridge this knowledge gap, we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive written reports, complemented by automatically generated plots, thereby enhancing the user experience and reducing the barriers to the adoption of this tool by the research community. Moreover, our approach can be expanded to other tools from the optimization community, showcasing the versatility and potential of LLMs in this field.
Paper Structure (24 sections, 3 equations, 5 figures, 2 tables)

This paper contains 24 sections, 3 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Search Trajectory Networks (STNs)
  4. Large Language Models (LLMs)
  5. Integrating LLMs into STNWeb
  6. Prompt Engineering
  7. Task A
  8. Task B
  9. Task C
  10. Feature Extraction
  11. Task A
  12. Task B
  13. Task C
  14. Empirical evaluation
  15. Setup
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Graphical overview on the automation of the analysis of optimization algorithm behavior using Large Language Models (LLMs).
  • Figure 2: Example of an STN produced by STNWeb.
  • Figure 3: Prompt templates that are automatically generated in STNWeb for each task.
  • Figure 4: Methodology to evaluate each LLM for every task.
  • Figure 5: Prompts and model output (used LLM: GPT-4-turbo) concerning the STN graphic shown in Figure \ref{['fig:stn-example']}.