Papers with outcome prediction models
On the Role of Negative Precedent in Legal Outcome Prediction (2023.tacl-1)
Copied to clipboard
| Challenge: | Legal outcome prediction is an increasingly popular task in AI. |
| Approach: | They propose to use the dynamics of a court process to develop two new models inspired by the dynamics. |
| Outcome: | The proposed model improves positive outcome prediction score to 77.15 F1 and doubles negative outcome prediction performance to 24.01 F1. |
Literature-Augmented Clinical Outcome Prediction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to clinical outcome prediction use only clinical notes and general biomedical literature. |
| Approach: | They propose to retrieve patient-specific medical literature and incorporate it into predictive models by combining clinical notes with language models. |
| Outcome: | The proposed approach boosts predictive performance on three important clinical tasks in comparison to strong LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%. |
PILOT: Legal Case Outcome Prediction with Case Law (2024.naacl-long)
Copied to clipboard
| Challenge: | predicting legal case outcomes requires identifying relevant precedent cases . predicting case outcomes in case law systems presents unique challenges . |
| Approach: | They propose a framework for making legal case outcome predictions with case law . they propose to use two modules for relevant case retrieval and temporal pattern handling . |
| Outcome: | The proposed framework shows significant improvement over previous models based on civil law cases . it is crucial to identify relevant precedent cases that serve as evidence for judges . |
Towards Explainability in Legal Outcome Prediction Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Current legal outcome prediction models do not explain their reasoning in the real world, but human legal actors need to understand the model’s decisions. |
| Approach: | They propose a method for identifying the precedent employed by legal outcome prediction models and a taxonomy of legal precedent to compare human judges and neural models. |
| Outcome: | The proposed model learns to predict outcomes reasonably well, but its use of precedent is unlike that of human judges. |