Better Call GPT, Comparing Large Language Models Against Lawyers

• URL: http://arxiv.org/abs/2401.16212v1
• Date: Wed, 24 Jan 2024 03:53:28 GMT
• Title: Better Call GPT, Comparing Large Language Models Against Lawyers
• Authors: Lauren Martin, Nick Whitehouse, Stephanie Yiu, Lizzie Catterson, Rivindu Perera (Onit AI Centre of Excellence)
• Abstract summary: This paper dissects whether Large Language Models can outperform humans in accuracy, speed, and cost efficiency during contract review. In speed, LLMs complete reviews in mere seconds, eclipsing the hours required by their human counterparts. Cost wise, LLMs operate at a fraction of the price, offering a staggering 99.97 percent reduction in cost over traditional methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a groundbreaking comparison between Large Language Models and traditional legal contract reviewers, Junior Lawyers and Legal Process Outsourcers. We dissect whether LLMs can outperform humans in accuracy, speed, and cost efficiency during contract review. Our empirical analysis benchmarks LLMs against a ground truth set by Senior Lawyers, uncovering that advanced models match or exceed human accuracy in determining legal issues. In speed, LLMs complete reviews in mere seconds, eclipsing the hours required by their human counterparts. Cost wise, LLMs operate at a fraction of the price, offering a staggering 99.97 percent reduction in cost over traditional methods. These results are not just statistics, they signal a seismic shift in legal practice. LLMs stand poised to disrupt the legal industry, enhancing accessibility and efficiency of legal services. Our research asserts that the era of LLM dominance in legal contract review is upon us, challenging the status quo and calling for a reimagined future of legal workflows.

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)
• Large Language Models: A Survey [69.72787936480394]
  Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
  arXiv  Detail & Related papers  (2024-02-09T05:37:09Z)
• BLT: Can Large Language Models Handle Basic Legal Text? [50.46167465931653]
  GPT-4, Claude, and PaLM 2 perform poorly at basic legal text handling. Fine-tuning for these tasks brings even a smaller model to near-perfect performance on our test set.
  arXiv  Detail & Related papers  (2023-11-16T09:09:22Z)
• Large Language Models are legal but they are not: Making the case for a powerful LegalLLM [0.0]
  The recent surge of Large Language Models (LLMs) has begun to provide new opportunities to apply NLP in the legal domain. We compare the zero-shot performance of three general-purpose LLMs (ChatGPT-20b, LLaMA-2-70b, and Falcon-180b) on the LEDGAR subset of the LexGLUE benchmark for contract provision classification. Although the LLMs were not explicitly trained on legal data, we observe that they are still able to classify the theme correctly in most cases.
  arXiv  Detail & Related papers  (2023-11-15T11:50:10Z)
• A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction [60.70089334782383]
  Large language models (LLMs) have demonstrated great potential for domain-specific applications. Recent disputes over GPT-4's law evaluation raise questions concerning their performance in real-world legal tasks. We design practical baseline solutions based on LLMs and test on the task of legal judgment prediction.
  arXiv  Detail & Related papers  (2023-10-18T07:38:04Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• LawBench: Benchmarking Legal Knowledge of Large Language Models [35.2812008533622]
  Large language models (LLMs) have demonstrated strong capabilities in various aspects. It is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels.
  arXiv  Detail & Related papers  (2023-09-28T09:35:59Z)
• Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence [5.07013500385659]
  This paper explores Large Language Models' (LLMs) capabilities in applying tax law. Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. Findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels.
  arXiv  Detail & Related papers  (2023-06-12T12:40:48Z)
• Legal Prompt Engineering for Multilingual Legal Judgement Prediction [2.539568419434224]
  Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing skill. We investigate the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights (in English) and the Federal Supreme Court of Switzerland (in German, French and Italian)
  arXiv  Detail & Related papers  (2022-12-05T12:17:02Z)
• 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)

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.