What question did this study set out to answer?

Synthesize current applications, methodological trends, and research gaps regarding large language models and transformer architectures within the legal domain.

January 1, 2025IEEE AccessOpen Access

Exploring LLMs Applications in Law: A Literature Review on Current Legal NLP Approaches

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Authors

MSMarco SiinoUniversity of Catania MFMariana FalcoUniversity of Palermo DCDaniele CroceConsorzio Nazionale Interuniversitario per le Telecomunicazioni

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Implication

Literature review reveals expanding adoption and improved performance of transformer models across legal tasks, suggesting growing utility for document analysis and case prediction.

Key Points

Synthesize current applications, methodological trends, and research gaps regarding large language models and transformer architectures within the legal domain.
Analyzed literature published between 2017 and 2023 retrieved via academic database searching and snowballing methodology.
Evaluated and categorized 61 selected publications focusing on transformer models across various legal workflows.
Identified primary application areas comprising legal document summarization, contract review, regulatory compliance, and case outcome prediction across a diversifying set of languages.
Observed consistent performance gains and heightened methodological sophistication in models utilizing Generative Pre-trained Transformer architectures for complex legal tasks.

Cite This Study

Siino et al. (2025) studied this question.

synapsesocial.com/papers/6a01c7d1449274ec075caff7 https://doi.org/10.1109/access.2025.3533217

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Implication

Literature review reveals expanding adoption and improved performance of transformer models across legal tasks, suggesting growing utility for document analysis and case prediction.

Exploring LLMs Applications in Law: A Literature Review on Current Legal NLP Approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

Exploring LLMs Applications in Law: A Literature Review on Current Legal NLP Approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

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