Large Language Models in Legislative Content Analysis: A Dataset from the Polish Parliament
- URL: http://arxiv.org/abs/2503.12100v1
- Date: Sat, 15 Mar 2025 12:10:20 GMT
- Title: Large Language Models in Legislative Content Analysis: A Dataset from the Polish Parliament
- Authors: Arkadiusz BryĆkowski, Jakub Klikowski,
- Abstract summary: The research contributes to the advancement of NLP in the legal field, particularly in the Polish language.<n>It has been demonstrated that even commonly accessible data can be practically utilized for legislative content analysis.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large language models (LLMs) are among the best methods for processing natural language, partly due to their versatility. At the same time, domain-specific LLMs are more practical in real-life applications. This work introduces a novel natural language dataset created by acquired data from official legislative authorities' websites. The study focuses on formulating three natural language processing (NLP) tasks to evaluate the effectiveness of LLMs on legislative content analysis within the context of the Polish legal system. Key findings highlight the potential of LLMs in automating and enhancing legislative content analysis while emphasizing specific challenges, such as understanding legal context. The research contributes to the advancement of NLP in the legal field, particularly in the Polish language. It has been demonstrated that even commonly accessible data can be practically utilized for legislative content analysis.
Related papers
- Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges [4.548047308860141]
This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 154 studies, with a final selection of 133 after manual filtering.
It explores foundational concepts related to NLP in the legal domain, illustrating the unique aspects and challenges of processing legal texts.
We provide an overview of NLP tasks specific to legal text, such as Legal Document Summarisation, legal Named Entity Recognition, Legal Question Answering, Legal Argument Mining, Legal Text Classification, and Legal Judgement Prediction.
arXiv Detail & Related papers (2024-10-25T01:17:02Z) - Leveraging Knowledge Graphs and LLMs to Support and Monitor Legislative Systems [0.0]
This work investigates how Legislative Knowledge Graphs and LLMs can synergize and support legislative processes.
To this aim, we develop Legis AI Platform, an interactive platform focused on Italian legislation that enhances the possibility of conducting legislative analysis.
arXiv Detail & Related papers (2024-09-20T06:21:03Z) - 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) - Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning [19.55121050697779]
We propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information.
D3LM incorporates an innovative graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm, enabling the generation of critical questions.
Our research also introduces a new English-language CVG dataset based on the US case law database.
arXiv Detail & Related papers (2024-06-05T19:47:35Z) - A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [51.8203871494146]
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing.
Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient.
This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
arXiv Detail & Related papers (2024-05-17T17:47:39Z) - Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey [1.0770079992809338]
The capabilities of Large Language Models (LLMs) are increasingly demonstrating unique roles in the legal sector.
This survey delves into the synergy between LLMs and the legal system, such as their applications in tasks like legal text comprehension, case retrieval, and analysis.
The survey showcases the latest advancements in fine-tuned legal LLMs tailored for various legal systems, along with legal datasets available for fine-tuning LLMs in various languages.
arXiv Detail & Related papers (2024-04-01T08:35:56Z) - Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets.<n>This survey delves into an important attribute of these datasets: the dialect of a language.<n>Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
arXiv Detail & Related papers (2024-01-11T03:04:38Z) - 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) - Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [48.70557995528463]
This guide aims to provide researchers and practitioners with valuable insights and best practices for working with Large Language Models.
We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios.
arXiv Detail & Related papers (2023-04-26T17:52:30Z) - A Short Survey of Viewing Large Language Models in Legal Aspect [0.0]
Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning.
The integration of LLMs into the legal field has also raised several legal problems, including privacy concerns, bias, and explainability.
arXiv Detail & Related papers (2023-03-16T08:01:22Z) - 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.