Summary of Abstract Operations Research Modeling Using Natural Language Inputs, by Junxuan Li et al.
Abstract Operations Research Modeling Using Natural Language Inputs
by Junxuan Li, Ryan Wickman, Sahil Bhatnagar, Raj Kumar Maity, Arko Mukherjee
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel methodology combines recent advances in Large Language Models (LLMs) with Operations Research (OR) to simplify the development of mathematical models. The proposed system, NL2OR, can create and edit OR solutions from non-expert user queries expressed using Natural Language, reducing the need for domain expertise and the time required to formulate a problem. By leveraging LLMs, this approach has the potential to democratize access to OR problem-solving, making it more accessible to a broader range of users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to solve Operations Research problems using computers. Instead of requiring experts in mathematics and computer science, this method uses special language models to create solutions from simple descriptions written in everyday language. This makes it easier for people without extensive training to use mathematical programming tools, which can be useful in many fields such as logistics, finance, and resource management. |
