Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal
Scenarios Like a Lawyer?
- URL: http://arxiv.org/abs/2310.14880v2
- Date: Fri, 3 Nov 2023 03:50:07 GMT
- Title: Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal
Scenarios Like a Lawyer?
- Authors: Xiaoxi Kang, Lizhen Qu, Lay-Ki Soon, Adnan Trakic, Terry Yue Zhuo,
Patrick Charles Emerton, Genevieve Grant
- Abstract summary: ChatGPT is applied to perform analysis on the corpus using the IRAC method.
Each scenario in the corpus is annotated with a complete IRAC analysis in a semi-structured format.
In addition, we conducted the first empirical assessment of ChatGPT for IRAC analysis in order to understand how well it aligns with the analysis of legal professionals.
- Score: 14.103170412148584
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs), such as ChatGPT, have drawn a lot of attentions
recently in the legal domain due to its emergent ability to tackle a variety of
legal tasks. However, it is still unknown if LLMs are able to analyze a legal
case and perform reasoning in the same manner as lawyers. Therefore, we
constructed a novel corpus consisting of scenarios pertain to Contract Acts
Malaysia and Australian Social Act for Dependent Child. ChatGPT is applied to
perform analysis on the corpus using the IRAC method, which is a framework
widely used by legal professionals for organizing legal analysis. Each scenario
in the corpus is annotated with a complete IRAC analysis in a semi-structured
format so that both machines and legal professionals are able to interpret and
understand the annotations. In addition, we conducted the first empirical
assessment of ChatGPT for IRAC analysis in order to understand how well it
aligns with the analysis of legal professionals. Our experimental results shed
lights on possible future research directions to improve alignments between
LLMs and legal experts in terms of legal reasoning.
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) - Bridging Law and Data: Augmenting Reasoning via a Semi-Structured Dataset with IRAC methodology [22.740895683854568]
This paper introduces LEGALSEMI, a benchmark specifically curated for legal scenario analysis.
LEGALSEMI comprises 54 legal scenarios, each rigorously annotated by legal experts, based on the comprehensive IRAC (Issue, Rule, Application, Conclusion) framework.
A series of experiments were conducted to assess the usefulness of LEGALSEMI for IRAC analysis.
arXiv Detail & Related papers (2024-06-19T04:59:09Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - 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) - 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) - Unlocking Practical Applications in Legal Domain: Evaluation of GPT for
Zero-Shot Semantic Annotation of Legal Texts [0.0]
We evaluate the capability of a state-of-the-art generative pre-trained transformer (GPT) model to perform semantic annotation of short text snippets.
We found that the GPT model performs surprisingly well in zero-shot settings on diverse types of documents.
arXiv Detail & Related papers (2023-05-08T01:55:53Z) - 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) - Legal Sentiment Analysis and Opinion Mining (LSAOM): Assimilating
Advances in Autonomous AI Legal Reasoning [0.0]
Legal Sentiment Analysis and Opinion Mining (LSAOM) consists of two often intertwined phenomena and actions underlying legal discussions and narratives.
Efforts to undertake LSAOM have historically been performed by human hand and cognition.
Advances in Artificial Intelligence (AI) involving especially Natural Language Processing (NLP) and Machine Learning (ML) are bolstering how automation can systematically perform either or both of Sentiment Analysis and Opinion Mining.
arXiv Detail & Related papers (2020-10-02T04:15:21Z) - How Does NLP Benefit Legal System: A Summary of Legal Artificial
Intelligence [81.04070052740596]
Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain.
This paper introduces the history, the current state, and the future directions of research in LegalAI.
arXiv Detail & Related papers (2020-04-25T14:45:15Z)
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.