PILOT: Legal Case Outcome Prediction with Case Law
CoRR(2024)
摘要
Machine learning shows promise in predicting the outcome of legal cases, but
most research has concentrated on civil law cases rather than case law systems.
We identified two unique challenges in making legal case outcome predictions
with case law. First, it is crucial to identify relevant precedent cases that
serve as fundamental evidence for judges during decision-making. Second, it is
necessary to consider the evolution of legal principles over time, as early
cases may adhere to different legal contexts.
In this paper, we proposed a new model named PILOT (PredictIng Legal case
OuTcome) for case outcome prediction. It comprises two modules for relevant
case retrieval and temporal pattern handling, respectively. To benchmark the
performance of existing legal case outcome prediction models, we curated a
dataset from a large-scale case law database. We demonstrate the importance of
accurately identifying precedent cases and mitigating the temporal shift when
making predictions for case law, as our method shows a significant improvement
over the prior methods that focus on civil law case outcome predictions.
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