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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07272 |
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| _version_ | 1866915124836564992 |
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| author | Li, Junxuan Wickman, Ryan Bhatnagar, Sahil Maity, Raj Kumar Mukherjee, Arko |
| author_facet | Li, Junxuan Wickman, Ryan Bhatnagar, Sahil Maity, Raj Kumar Mukherjee, Arko |
| contents | Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07272 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Abstract Operations Research Modeling Using Natural Language Inputs Li, Junxuan Wickman, Ryan Bhatnagar, Sahil Maity, Raj Kumar Mukherjee, Arko Artificial Intelligence Human-Computer Interaction Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems. |
| title | Abstract Operations Research Modeling Using Natural Language Inputs |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.07272 |