LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text (2024.eacl-long)
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Dor Bernsohn, Gil Semo, Yaron Vazana, Gila Hayat, Ben Hagag, Joel Niklaus, Rohit Saha, Kyryl Truskovskyi
| Challenge: | a recent study focused on detecting legal violations within unstructured textual data . a similar study focused only on associating violations with potentially affected individuals . |
| Approach: | They constructed two datasets using Large Language Models (LLMs) they publicize the results to advance legal natural language processing research . |
| Outcome: | The proposed datasets and the code used for the experiments have been released to advance legal natural language processing (NLP) |
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