SPEAR-MM: Selective Parameter Evaluation and Restoration via Model Merging for Efficient Financial LLM Adaptation

Overview

SPEAR-MM is a framework designed to enhance the adaptation of large language models (LLMs) to financial domains without losing general reasoning capabilities. It achieves this by selectively evaluating and restoring model parameters through a method called spherical interpolation merging.

Key Insights

• Retention of Capabilities: SPEAR-MM retains 91.2% of general reasoning capabilities, compared to 69.7% with standard continual pretraining.
  • Evidence: Applied to LLaMA-3.1-8B for financial tasks.
  • Verifiable: Yes, through application results.
• Domain Adaptation Gains: Maintains 94% of domain-specific adaptation gains.
  • Evidence: Performance metrics from financial task applications.
  • Verifiable: Yes, through application results.
• Cost Efficiency: Reduces computational costs by 90%.
  • Evidence: Computational cost analysis.
  • Verifiable: Yes, through cost analysis.

BFSI Relevance

• Why Relevant: The framework allows financial institutions to adapt LLMs efficiently, preserving essential reasoning capabilities while focusing on domain-specific tasks.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Corporate Banking
• Actionable Implications:
  • Evaluate the adoption of SPEAR-MM for cost-effective LLM adaptation.
  • Consider integrating SPEAR-MM to enhance model efficiency and capability retention.