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This document analyzes the effectiveness of multi-task fine-tuning for large language models (LLMs) in finance, revealing that training on a combination of related tasks can enhance performance more than focusing solely on a single target task. The study demonstrates that a smaller model, Phi-3-Mini, can outperform larger models like GPT-4-o on financial benchmarks through this approach, supported by extensive empirical testing. Additionally, the research highlights the importance of incorporating general i

Author

memej92844

Language

EN