Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model
This addresses the problem of limited annotated data in legal document drafting for legal professionals, though it is incremental as it applies existing fine-tuning methods to a new domain.
The paper tackles the challenge of drafting legal documents by fine-tuning a pre-trained large language model using annotation-free legal documents, achieving the task on a local computer to protect privacy and improve security.
With the development of large-scale Language Models (LLM), fine-tuning pre-trained LLM has become a mainstream paradigm for solving downstream tasks of natural language processing. However, training a language model in the legal field requires a large number of legal documents so that the language model can learn legal terminology and the particularity of the format of legal documents. The typical NLP approaches usually rely on many manually annotated data sets for training. However, in the legal field application, it is difficult to obtain a large number of manually annotated data sets, which restricts the typical method applied to the task of drafting legal documents. The experimental results of this paper show that not only can we leverage a large number of annotation-free legal documents without Chinese word segmentation to fine-tune a large-scale language model, but more importantly, it can fine-tune a pre-trained LLM on the local computer to achieve the generating legal document drafts task, and at the same time achieve the protection of information privacy and to improve information security issues.