用神经启发架构提升大模型在特定领域快速适应能力
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
- 采用神经启发推理网络结构，结合提示工程与上下文学习实现快速适配
- 在半导体版图设计任务中显著优于基线大模型，接近o1-preview性能
- 适合需要持续学习和动态适应的工业级智能系统研发人员
本文提出SOLOMON——一种新型神经启发的大语言模型推理网络架构，旨在提升基础模型在特定领域的适应能力。以半导体版图设计为例，通过提示工程与上下文学习技术，验证了SOLOMON可实现通用大模型向专用任务的快速迁移。实验揭示大模型在空间推理及领域知识应用方面的挑战。结果表明，SOLOMON实例显著优于基线大模型，性能接近当前最优推理模型o1-preview。论文进一步探讨了可持续学习、动态演化的自适应AI系统未来研究方向。
原文摘要 · Abstract (English)
This paper presents SOLOMON, a novel Neuro-inspired Large Language Model (LLM) Reasoning Network architecture that enhances the adaptability of foundation models for domain-specific applications. Through a case study in semiconductor layout design, we demonstrate how SOLOMON enables swift adaptation of general-purpose LLMs to specialized tasks by leveraging Prompt Engineering and In-Context Learning techniques. Our experiments reveal the challenges LLMs face in spatial reasoning and applying domain knowledge to practical problems. Results show that SOLOMON instances significantly outperform their baseline LLM counterparts and achieve performance comparable to state-of-the-art reasoning model, o1-preview. We discuss future research directions for developing more adaptive AI systems that can continually learn, adapt, and evolve in response to new information and changing requirements.
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