In the era of Large Language Models (LLMs), predicting judicial outcomes
poses significant challenges due to the complexity of legal proceedings and the
scarcity of expert-annotated datasets. Addressing this, we introduce
\textbf{Pred}iction with \textbf{Ex}planation (\texttt{PredEx}), the largest
expert-annotated dataset for legal judgment prediction and explanation in the
Indian context, featuring over 15,000 annotations. This groundbreaking corpus
significantly enhances the training and evaluation of AI models in legal
analysis, with innovations including the application of instruction tuning to
LLMs. This method has markedly improved the predictive accuracy and explanatory
depth of these models for legal judgments. We employed various
transformer-based models, tailored for both general and Indian legal contexts.
Through rigorous lexical, semantic, and expert assessments, our models
effectively leverage \texttt{PredEx} to provide precise predictions and
meaningful explanations, establishing it as a valuable benchmark for both the
legal profession and the NLP community.

在大型语言模型（LLMs）时代，提供了一个在印度背景下针对法律判决进行预测和解释的最大专家标注数据集（PredEx），该数据集具有超过 15,000 个注释。通过使用调整指令来提高 LLMs 的预测准确性和解释深度，我们表明这一方法显著提升了此类模型在法律判决上的预测准确性和解释深度，并且我们的模型通过严格的词法、语义和专家评估，有效地利用 PredEx 来提供精确的预测和有意义的解释，将其确定为法律专业和自然语言处理社区的有价值的基准。

重新想象的法律判断：PredEx 与智能人工智能在印度法院的崛起

Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI  Interpretation in Indian Courts

We propose and evaluate an automated pipeline for discovering significant
topics from legal decision texts by passing features synthesized with topic
models through penalised regressions and post-selection significance tests. The
method identifies case topics significantly correlated with outcomes,
topic-word distributions which can be manually-interpreted to gain insights
about significant topics, and case-topic weights which can be used to identify
representative cases for each topic. We demonstrate the method on a new dataset
of domain name disputes and a canonical dataset of European Court of Human
Rights violation cases. Topic models based on latent semantic analysis as well
as language model embeddings are evaluated. We show that topics derived by the
pipeline are consistent with legal doctrines in both areas and can be useful in
other related legal analysis tasks.

通过自动化流程从法律决策文本中发现相关主题，方法包括使用主题模型生成特征，并通过惩罚回归和后选择显著性检验进行评估；实验证明该方法在域名争议和欧洲人权法院侵权案例数据集上识别出与结果显著相关的案例主题、可以手动解释的主题 - 词分布以及可以用于找出每个主题的代表性案例的案例 - 主题权重；该方法产生的主题与两个领域的法律原则一致，可在其他相关法律分析任务中发挥作用。

从法律裁决中发现重要话题和选择性推论

Discovering Significant Topics from Legal Decisions with Selective  Inference

Better understanding of Large Language Models' (LLMs) legal analysis
abilities can contribute to improving the efficiency of legal services,
governing artificial intelligence, and leveraging LLMs to identify
inconsistencies in law. This paper explores LLM capabilities in applying tax
law. We choose this area of law because it has a structure that allows us to
set up automated validation pipelines across thousands of examples, requires
logical reasoning and maths skills, and enables us to test LLM capabilities in
a manner relevant to real-world economic lives of citizens and companies. Our
experiments demonstrate emerging legal understanding capabilities, with
improved performance in each subsequent OpenAI model release. We experiment
with retrieving and utilising the relevant legal authority to assess the impact
of providing additional legal context to LLMs. Few-shot prompting, presenting
examples of question-answer pairs, is also found to significantly enhance the
performance of the most advanced model, GPT-4. The findings indicate that LLMs,
particularly when combined with prompting enhancements and the correct legal
texts, can perform at high levels of accuracy but not yet at expert tax lawyer
levels. As LLMs continue to advance, their ability to reason about law
autonomously could have significant implications for the legal profession and
AI governance.

本篇论文探讨了大型语言模型（LLMs）在应用税法方面的能力，发现使用少量提示可以显着提高最先进的模型性能，但它们仍不能像专业的税务律师那样进行推理和判断。