Legal Prompt Engineering for Multilingual Legal Judgement Prediction

• URL: http://arxiv.org/abs/2212.02199v1
• Date: Mon, 5 Dec 2022 12:17:02 GMT
• Title: Legal Prompt Engineering for Multilingual Legal Judgement Prediction
• Authors: Dietrich Trautmann, Alina Petrova, Frank Schilder
• Abstract summary: 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)
• Score: 2.539568419434224
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal Judgement Prediction (LJP) task. 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). Our results show that zero-shot LPE is better compared to the baselines, but it still falls short compared to current state of the art supervised approaches. Nevertheless, the results are important, since there was 1) no explicit domain-specific data used - so we show that the transfer to the legal domain is possible for general-purpose LLMs, and 2) the LLMs where directly applied without any further training or fine-tuning - which in turn saves immensely in terms of additional computational costs.

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)
• Better Call GPT, Comparing Large Language Models Against Lawyers [0.0]
  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.
  arXiv  Detail & Related papers  (2024-01-24T03:53:28Z)
• BLT: Can Large Language Models Handle Basic Legal Text? [44.89873147675516]
  GPT-4 and Claude perform poorly on basic legal text handling. Poor performance on benchmark casts into doubt their reliability as-is for legal practice. Fine-tuning on training set brings even a small model to near-perfect performance.
  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)
• LAiW: A Chinese Legal Large Language Models Benchmark [17.66376880475554]
  General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. We are the first to build the Chinese legal LLMs benchmark LAiW, based on the logic of legal practice.
  arXiv  Detail & Related papers  (2023-10-09T11:19:55Z)
• 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)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• 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.