Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy
It provides actionable insights for businesses and researchers on adapting LLMs for specialized contexts, though it is incremental in nature.
This paper tackles the problem of balancing general and specialized skills in large language models for monetization tasks by introducing a methodology that blends in-domain and general-purpose data during fine-tuning and evaluates performance with 45 tailored questions, resulting in validated frameworks that guide efficient resource allocation.
This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks. The goal is to balance general language proficiency with domain-specific skills. The methodology has three main components: 1) Carefully blending in-domain and general-purpose data during fine-tuning to achieve an optimal balance between general and specialized capabilities; 2) Designing a comprehensive evaluation framework with 45 questions tailored to assess performance on functionally relevant dimensions like reliability, consistency, and business impact; 3) Analyzing how model size and continual training influence metrics to guide efficient resource allocation during fine-tuning. The paper details the design, data collection, analytical techniques, and results validating the proposed frameworks. It aims to provide businesses and researchers with actionable insights on effectively adapting LLMs for specialized contexts. We also intend to make public the comprehensive evaluation framework, which includes the 45 tailored questions and their respective scoring guidelines, to foster transparency and collaboration in adapting LLMs for specialized tasks.