Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and Resolution
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Abstract (arXiv)
In this work, we present two systems -- Named Entity Resolution (NER) and Natural Language Inference (NLI) -- for detecting legal violations within unstructured textual data and for associating these violations with potentially affected individuals, respectively. Both these systems are lightweight DeBERTa based encoders that outperform the LLM baselines. The proposed NER system achieved an F1 score of 60.01% on Subtask A of the LegalLens challenge, which focuses on identifying violations. The proposed NLI system achieved an F1 score of 84.73% on Subtask B of the LegalLens challenge, which focuses on resolving these violations by matching them with pre-existing legal complaints of class action cases. Our NER system ranked sixth and NLI system ranked fifth on the LegalLens leaderboard. We release the trained models and inference scripts.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 30 Oct 2024.
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References · 16
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4 more
- 2024A synthetic data approach for domain generalization of NLI modelscited by 15
- 2024LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Textcited by 12
- 2023SemEval 2023 Task 6: LegalEval - Understanding Legal Textscited by 7
- 2024LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Textcited by 5
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Citing papers per year: 2026: 1.
1 paper.