Effective prompting with ChatGPT for problem formulation in engineering optimization
Why this work is in the frame
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Bibliographic record
Abstract
Optimization problem formulation, a crucial but manually performed process, can present an obstacle to applying optimization in engineering since many practitioners find it challenging. This article explores the use of ChatGPT to address this challenge. It evaluates the efficacy of self-designed prompts with different ChatGPT models. Using analysis of variance and Tukey's test, assessments are conducted to determine the influence of variations in wording on the quality of solutions. The sequential learning approach is also tested to assess its impact on ChatGPT responses. This article confirms the importance of specificity in word choice and the relevance of domain-specific engineering terminology in crafting prompts for problem modelling. The analysis shows that a combination of properly selected words can lead to high-quality optimization problem formulations. Furthermore, it is found that sequential learning can enhance formulations. This work may bring more attention to the use of ChatGPT for formulating problems in engineering optimization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it