An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems

Hashmath Shaik, Alex Doboli
12/31/2024
cs.CLcs.AI

No official code found. Best implementation: lukecage0/ReasonMesh (1 star, last commit 2025-03-01).

Abstract

Large Language Models offer new opportunities to devise automated implementation generation methods that can tackle problem solving activities beyond traditional methods, which require algorithmic specifications and can use only static domain knowledge, like performance metrics and libraries of basic building blocks. Large Language Models could support creating new methods to support problem solving activities for open-ended problems, like problem framing, exploring possible solving approaches, feature elaboration and combination, more advanced implementation assessment, and handling unexpected situations. This report summarized the current work on Large Language Models, including model prompting, Reinforcement Learning, and Retrieval-Augmented Generation. Future research requirements were also discussed.

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Code Implementations(1)

lukecage0/ReasonMeshCommunity52%

A repo with latest reasoning techniques implemented form the overview section from https://arxiv.org/abs/2501.00562

10PythonFeb 23, 20251 years ago

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Cite this paper

@article{shaik2024overview,
  title  = {An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems},
  author = {Hashmath Shaik and Alex Doboli},
  year   = {2024},
  eprint = {2501.00562},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.00562v2}
}

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