Investigating the Potential of Using Large Language Models for Scheduling
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911914113630208 |
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| author | Jobson, Deddy Li, Yilin |
| author_facet | Jobson, Deddy Li, Yilin |
| contents | The inaugural ACM International Conference on AI-powered Software introduced the AIware Challenge, prompting researchers to explore AI-driven tools for optimizing conference programs through constrained optimization. We investigate the use of Large Language Models (LLMs) for program scheduling, focusing on zero-shot learning and integer programming to measure paper similarity. Our study reveals that LLMs, even under zero-shot settings, create reasonably good first drafts of conference schedules. When clustering papers, using only titles as LLM inputs produces results closer to human categorization than using titles and abstracts with TFIDF. The code has been made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating the Potential of Using Large Language Models for Scheduling Jobson, Deddy Li, Yilin Artificial Intelligence Machine Learning The inaugural ACM International Conference on AI-powered Software introduced the AIware Challenge, prompting researchers to explore AI-driven tools for optimizing conference programs through constrained optimization. We investigate the use of Large Language Models (LLMs) for program scheduling, focusing on zero-shot learning and integer programming to measure paper similarity. Our study reveals that LLMs, even under zero-shot settings, create reasonably good first drafts of conference schedules. When clustering papers, using only titles as LLM inputs produces results closer to human categorization than using titles and abstracts with TFIDF. The code has been made publicly available. |
| title | Investigating the Potential of Using Large Language Models for Scheduling |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.07573 |