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

arXiv 2501.00562 ↗First posted 31 Dec 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 112 of its 157 references that are in the map. First posted on arXiv 31 Dec 2024.

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112 of 157 references.

References · 112

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38 more
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  1. 2023Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
  2. 2025Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey
  3. 2024Plan of Thoughts: Heuristic-Guided Problem Solving with Large Language Models
  4. 2024Eliciting Problem Specifications via Large Language Models
  5. 2024Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
  6. 2024Multi-Step Reasoning with Large Language Models, a Survey
  7. 2024Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
  8. 2025Probing Large Language Models in Reasoning and Translating Complex Linguistic Puzzles