Authors: T.Y.S.S. Santosh, Irtiza Chowdhury, Shanshan Xu, Matthias Grabmair

Published on: September 27, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2409.18645

Summary

• What is new: This is the first systematic exploration of selective prediction in the field of legal NLP, focusing on how different design choices impact the reliability of models used for Case Outcome Classification.
• Why this is important: High-stakes decisions in legal contexts need reliable confidence estimates from NLP models to prevent significant errors.
• What the research proposes: Investigate how pre-training corpus, confidence estimators, and fine-tuning losses affect model reliability, and implement techniques like Monte Carlo dropout and confident error regularization to improve prediction confidence.
• Results: Diverse, domain-specific pre-training improves calibration; larger models tend to be overconfident; Monte Carlo dropout methods provide reliable confidence estimates and confident error regularization reduces overconfidence.

Technical Details

Technological frameworks used: Selective prediction framework

Models used: Monte Carlo dropout, confident error regularization

Data used: European Court of Human Rights (ECtHR) cases

Potential Impact

Legal tech firms and industries that rely on legal decision-support systems could benefit from improved model reliability and confidence estimation.

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