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This paper introduces a multifaceted methodology for fine-tuning andevaluating large language models (LLMs) for specialized monetization tasks. Thegoal is to balance general language proficiency with domain-specific skills.The methodology has three main components: 1) Carefully blending in-domain andgeneral-purpose data during fine-tuning to achieve an optimal balance betweengeneral and specialized capabilities; 2) Designing a comprehensive evaluationframework with 45 questions tailored to assess performance on functionallyrelevant dimensions like reliability, consistency, and business impact; 3)Analyzing how model size and continual training influence metrics to guideefficient resource allocation during fine-tuning. The paper details the design,data collection, analytical techniques, and results validating the proposedframeworks. It aims to provide businesses and researchers with actionableinsights on effectively adapting LLMs for specialized contexts. We also intendto make public the comprehensive evaluation framework, which includes the 45tailored questions and their respective scoring guidelines, to fostertransparency and collaboration in adapting LLMs for specialized tasks.

Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy