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.

我们提出了一种名为 PILOT（PredictIng Legal case OuTcome）的新模型，用于案例结果预测。该模型由相关案例检索和时间模式处理两个模块组成，通过对大规模案例数据库进行实验，我们展示了准确识别先例案例和缓解时间转移对案例法预测的重要性，并显示出显著改进，超过之前专注于民事案件结果预测的方法。

PILOT：案例法律结果预测

PILOT: Legal Case Outcome Prediction with Case Law

Interpreting the meaning of legal open-textured terms is a key task of legal
professionals. An important source for this interpretation is how the term was
applied in previous court cases. In this paper, we evaluate the performance of
GPT-4 in generating factually accurate, clear and relevant explanations of
terms in legislation. We compare the performance of a baseline setup, where
GPT-4 is directly asked to explain a legal term, to an augmented approach,
where a legal information retrieval module is used to provide relevant context
to the model, in the form of sentences from case law. We found that the direct
application of GPT-4 yields explanations that appear to be of very high quality
on their surface. However, detailed analysis uncovered limitations in terms of
the factual accuracy of the explanations. Further, we found that the
augmentation leads to improved quality, and appears to eliminate the issue of
hallucination, where models invent incorrect statements. These findings open
the door to the building of systems that can autonomously retrieve relevant
sentences from case law and condense them into a useful explanation for legal
scholars, educators or practicing lawyers alike.

对 GPT-4 在生成有关立法术语方面的准确性、清晰性和相关性上的表现进行评估。与基准设置相比，采用法律信息检索模块提供以前案例中的语句作为模型的上下文可以获得更好的结果，从而可以自主检索相关案例，并将这些案例中有用的语句压缩成有用的法律解释。