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

2024-12-31

AI Summary

Large language models (LLMs) exhibit limited autonomous capability in solving open-ended problems, primarily due to overreliance on explicit algorithms and static knowledge. Method: We propose a novel end-to-end paradigm—spanning problem framing, solution exploration, implementation generation, and strategy assessment—that integrates prompt engineering, retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF). This synergy enhances LLMs’ proficiency in feature composition, dynamic anomaly response, and high-level strategy evaluation. Contribution/Results: We present the first systematic taxonomy of paradigm evolution for LLM-based implementation generation, identify critical technical bottlenecks, and establish a theoretical framework and technology roadmap for autonomous problem solving. Our work advances the development of LLM-driven general-purpose agents by enabling more robust, adaptive, and self-assessing reasoning capabilities.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsMultiagent Systems: Distributed Problem Solving

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Large Language Models
• Complex Problem Solving
• Open-ended Questions

Innovation

• Large Language Models
• Automated Problem-Solving
• Reinforcement Learning