A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification

• URL: http://arxiv.org/abs/2411.02476v1
• Date: Mon, 04 Nov 2024 18:06:36 GMT
• Title: A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification
• Authors: Sorouralsadat Fatemi, Yuheng Hu, Maryam Mousavi,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks. This study investigates the efficacy of instruction fine-tuning to enhance their performance in financial text classification tasks.
• Score: 0.8192907805418583
• License:
• Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tasks due to the technical and specialized nature of financial texts. This study investigates the efficacy of instruction fine-tuning smaller-scale LLMs, including Mistral-7B, Llama3-8B, and Phi3-mini, to enhance their performance in financial text classification tasks. We fine-tuned both instruction-tuned and base models across four financial classification tasks, achieving significant improvements in task-specific performance. Furthermore, we evaluated the zero-shot capabilities of these fine-tuned models on three unseen complex financial tasks, including argument classification, deal completeness classification, and causal classification. Our results indicate while base model fine-tuning led to greater degradation, instruction-tuned models maintained more robust performance. To address this degradation, we employed model merging techniques, integrating single-task domain-specific fine-tuned models with the base model. Using this merging method resulted in significant enhancements in zero-shot performance, even exceeding the original model's accuracy on certain datasets. Our findings underscore the effectiveness of instruction fine-tuning and model merging for adapting LLMs to specialized financial text classification tasks.

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