A systematic assessment of Large Language Models for constructing two-level fractional factorial designs
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910050127183872 |
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| author | Vazquez, Alan R. Rother, Kilian M. Charles-Gonzalez, Marco V. |
| author_facet | Vazquez, Alan R. Rother, Kilian M. Charles-Gonzalez, Marco V. |
| contents | Two-level fractional factorial designs permit the study multiple factors using a limited number of runs. Traditionally, these designs are obtained from catalogs available in standard textbooks or statistical software. However, modern Large Language Models (LLMs) can now produce two-level fractional factorial designs, but the quality of these designs has not been previously assessed. In this paper, we perform a systematic evaluation of two popular classes of LLMs, namely GPT and Gemini models, to construct two-level fractional factorial designs with 8, 16, and 32 runs, and 4 to 26 factors. To this end, we use prompting techniques to develop a high-quality set of design construction tasks for the LLMs. We compare the designs obtained by the LLMs with the best-known designs in terms of resolution and minimum aberration criteria. We show that the LLMs can effectively construct optimal 8-, 16-, and 32-run designs with up to eight factors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A systematic assessment of Large Language Models for constructing two-level fractional factorial designs Vazquez, Alan R. Rother, Kilian M. Charles-Gonzalez, Marco V. Methodology Computation Two-level fractional factorial designs permit the study multiple factors using a limited number of runs. Traditionally, these designs are obtained from catalogs available in standard textbooks or statistical software. However, modern Large Language Models (LLMs) can now produce two-level fractional factorial designs, but the quality of these designs has not been previously assessed. In this paper, we perform a systematic evaluation of two popular classes of LLMs, namely GPT and Gemini models, to construct two-level fractional factorial designs with 8, 16, and 32 runs, and 4 to 26 factors. To this end, we use prompting techniques to develop a high-quality set of design construction tasks for the LLMs. We compare the designs obtained by the LLMs with the best-known designs in terms of resolution and minimum aberration criteria. We show that the LLMs can effectively construct optimal 8-, 16-, and 32-run designs with up to eight factors. |
| title | A systematic assessment of Large Language Models for constructing two-level fractional factorial designs |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2512.17113 |