无需指令数据构建金融领域指令微调大语言模型：基于持续预训练与模型合并
The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging
AI总结：
本文提出将金融领域持续预训练向量与公开指令微调模型的任务向量合并，在无需额外指令数据的情况下高效构建金融指令微调LLMs，并验证了其有效性。
摘要：
本文提出了一种在无需指令数据的情况下构建金融领域指令微调大语言模型（LLMs）的新方法。传统上，开发此类领域专用LLMs资源消耗巨大，需要大规模数据集和大量算力来进行持续预训练与指令微调。本研究提出了一种更简单的方法，将领域专用持续预训练与模型合并相结合。鉴于通用预训练LLMs及其指令微调版本通常都是公开可用的，可以利用它们来获得所需的指令任务向量。通过将该向量与领域专用预训练向量合并，我们能够在不使用额外指令数据的情况下有效构建金融领域指令微调LLMs。我们的流程包括两个步骤：首先，在金融数据上进行持续预训练；其次，将指令微调向量与领域专用预训练向量合并。实验证明了金融领域指令微调LLMs的成功构建。我们方法的一个主要优势在于指令微调向量与领域专用预训练向量几乎相互独立，这种独立性使得我们的方法非常有效。本研究中开发的日语金融指令微调LLMs可在 https://huggingface.co/pfnet/nekomata-14b-pfn-qfin-inst-merge 获取。
英文摘要：
This paper proposes a novel method for constructing instruction-tuned large language models (LLMs) for finance without instruction data. Traditionally, developing such domain-specific LLMs has been resource-intensive, requiring a large dataset and significant computational power for continual pretraining and instruction tuning. Our study proposes a simpler approach that combines domain-specific continual pretraining with model merging. Given that general-purpose pretrained LLMs and their instruction-tuned LLMs are often publicly available, they can be leveraged to obtain the necessary instruction task vector. By merging this with a domain-specific pretrained vector, we can effectively create instruction-tuned LLMs for finance without additional instruction data. Our process involves two steps: first, we perform continual pretraining on financial data; second, we merge the instruction-tuned vector with the domain-specific pretrained vector. Our experiments demonstrate the successful construction of instruction-tuned LLMs for finance. One major advantage of our method is that the instruction-tuned and domain-specific pretrained vectors are nearly independent. This independence makes our approach highly effective. The Japanese financial instruction-tuned LLMs we developed in this study are available at https://huggingface.co/pfnet/nekomata-14b-pfn-qfin-inst-merge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
