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Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications

Bo Wen, Xin Zhang

TL;DR

This work presents SOLOMON, a neuro-inspired LLM reasoning network designed to adapt general-purpose foundation models to domain-specific tasks, demonstrated through semiconductor layout design. The architecture combines a pool of Thought Generators, a Thought Assessor for self-reflection and error mitigation, and a Steering Subsystem driven by Prompt Engineering to quickly align with domain requirements. Empirical results on 25 layout tasks show that SOLOMON consistently improves over baseline LLMs, reducing runtime and scaling errors and achieving performance comparable to state-of-the-art reasoning models in several categories. The study underscores the importance of enhanced reasoning and multi-agent oversight for practical AI adaptation, and outlines future directions such as hierarchical layering and broader-domain applications.

Abstract

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.

Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications

TL;DR

This work presents SOLOMON, a neuro-inspired LLM reasoning network designed to adapt general-purpose foundation models to domain-specific tasks, demonstrated through semiconductor layout design. The architecture combines a pool of Thought Generators, a Thought Assessor for self-reflection and error mitigation, and a Steering Subsystem driven by Prompt Engineering to quickly align with domain requirements. Empirical results on 25 layout tasks show that SOLOMON consistently improves over baseline LLMs, reducing runtime and scaling errors and achieving performance comparable to state-of-the-art reasoning models in several categories. The study underscores the importance of enhanced reasoning and multi-agent oversight for practical AI adaptation, and outlines future directions such as hierarchical layering and broader-domain applications.

Abstract

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.

Paper Structure

This paper contains 32 sections, 5 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Neuro-inspired LLM Reasoning Network Architecture
  3. Thought Generators:
  4. Thought Assessor:
  5. Steering Subsystem:
  6. Problem: Spatial Reasoning and Domain Knowledge Application
  7. SOLOMON Performance and Comparison
  8. Conclusion and Future Work
  9. Open Source Code and Dataset:
  10. Acknowledgment:
  11. Appendix
  12. Open Source Code and Dataset
  13. Task Prompts and Baseline LLM Performance
  14. Errors in Baseline Experiment
  15. Scaling Errors
  16. ...and 17 more sections

Figures (5)

  • Figure 1: SOLOMON Architecture Diagram
  • Figure 2: Sketch input and ChatGPT-generated outputs for the via connection experiment. The sketch depicts a desired layout with two vias connected by a metal layer and circular contact pads on top. The outputs show the progression of ChatGPT's understanding and refinement of the layout based on iterative feedback and context provided by the user.
  • Figure 3: Performance comparison between SOLOMON instances, their baseline counterpart single LLMs, and o1-preview across different layout design task categories. Lighter colored bars on left represent baseline performance of individual LLMs, while darker bars on right show the performance of corresponding SOLOMON instances. O1-preview results serve as a benchmark for state-of-the-art reasoning performance.
  • Figure 4: Sketch used for via connection test cases
  • Figure 5: Test 3 - Iterations to guide the model to generate desired output