Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model

• URL: http://arxiv.org/abs/2406.04202v1
• Date: Thu, 6 Jun 2024 16:00:20 GMT
• Title: Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model
• Authors: Chun-Hsien Lin, Pu-Jen Cheng,
• Abstract summary: This paper shows that we can leverage a large number of annotation-free legal documents without Chinese word segmentation to fine-tune a large-scale language model. It can also achieve the generating legal document drafts task, and at the same time achieve the protection of information privacy and to improve information security issues.
• Score: 1.3812010983144798
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• SCALE: Scaling up the Complexity for Advanced Language Model Evaluation [19.339580164451256]
  We introduce a novel NLP benchmark that poses challenges to current Large Language Models (LLMs) Our benchmark comprises diverse legal NLP datasets from the Swiss legal system. As part of our study, we evaluate several pre-trained multilingual language models on our benchmark to establish strong baselines as a point of reference.
  arXiv  Detail & Related papers  (2023-06-15T16:19:15Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension [6.442209435258797]
  LegalRelectra is a legal-domain language model trained on mixed-domain legal and medical corpora. Our training architecture implements the Electra framework, but utilizes Reformer instead of BERT for its generator and discriminator.
  arXiv  Detail & Related papers  (2022-12-16T00:15:14Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)
• LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models [8.745407715423992]
  Cross-lingual document representations enable language understanding in multilingual contexts. Large pre-trained language models such as BERT, XLM and XLM-RoBERTa have achieved great success when fine-tuned on sentence-level downstream tasks.
  arXiv  Detail & Related papers  (2021-06-07T07:14:00Z)
• Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
  We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
  arXiv  Detail & Related papers  (2021-05-09T09:39:25Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• Pre-training via Paraphrasing [96.79972492585112]
  We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual paraphrasing objective. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation.
  arXiv  Detail & Related papers  (2020-06-26T14:43:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.