指令微调大型语言模型用于金融文本分类的比较分析
A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification
AI总结：
本研究通过指令微调Mistral-7B、Llama3-8B和Phi3-mini等小型LLM，结合模型合并技术，显著提升了金融文本分类及零样本复杂任务性能。
摘要：
大型语言模型（LLMs）在包括语言理解、推理和生成在内的各种自然语言处理（NLP）任务中展现了令人印象深刻的能力。然而，由于金融文本的技术性和专业性，通用领域的LLMs往往在金融任务上表现不佳。本研究探讨了指令微调较小规模LLMs（包括Mistral-7B、Llama3-8B和Phi3-mini）以提升其在金融文本分类任务中性能的有效性。我们在四个金融分类任务上对指令微调模型和基础模型进行了微调，在任务特定性能上取得了显著提升。此外，我们评估了这些微调模型在三个未见过的复杂金融任务（包括论点分类、交易完整性分类和因果分类）上的零样本能力。我们的结果表明，虽然基础模型微调导致了更大的性能退化，但指令微调模型保持了更稳健的性能。为了解决这种退化，我们采用了模型合并技术，将单任务领域特定的微调模型与基础模型整合。使用这种合并方法在零样本性能上带来了显著提升，甚至在某些数据集上超过了原始模型的准确率。我们的发现强调了指令微调和模型合并在使LLMs适应专门金融文本分类任务方面的有效性。
英文摘要：
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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