Summary of The Large Language Model Greeklegalroberta, by Vasileios Saketos and Despina-athanasia Pantazi and Manolis Koubarakis
The Large Language Model GreekLegalRoBERTa
by Vasileios Saketos, Despina-Athanasia Pantazi, Manolis Koubarakis
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces four versions of GreekLegalRoBERTa, large language models trained on a combination of Greek legal and non-legal text. These models demonstrate superior performance compared to existing Greek LegalBERT variants in two tasks: named entity recognition (NER) and multi-class legal topic classification (MCLTC). The authors’ work contributes to the study of domain-specific NLP tasks in low-resource languages, such as Greek, utilizing modern techniques and methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can read and understand Greek legal documents. It creates four new language models that are trained on a mix of Greek law texts and other Greek writing. These models do a better job than previous ones at recognizing important words (like names) and grouping similar legal topics together. This is important because it shows that we can use modern computer techniques to help with tasks in languages like Greek where there isn’t as much data. |
Keywords
» Artificial intelligence » Classification » Named entity recognition » Ner » Nlp
