Traditionally in the domain of legal research, the retrieval of pertinent
citations from intricate case descriptions has demanded manual effort and
keyword-based search applications that mandate expertise in understanding legal
jargon. Legal case descriptions hold pivotal information for legal
professionals and researchers, necessitating more efficient and automated
approaches. We propose a methodology that combines natural language processing
(NLP) and machine learning techniques to enhance the organization and
utilization of legal case descriptions. This approach revolves around the
creation of textual embeddings with the help of state-of-art embedding models.
Our methodology addresses two primary objectives: unsupervised clustering and
supervised citation retrieval, both designed to automate the citation
extraction process. Although the proposed methodology can be used for any
dataset, we employed the Supreme Court of The United States (SCOTUS) dataset,
yielding remarkable results. Our methodology achieved an impressive accuracy
rate of 90.9%. By automating labor-intensive processes, we pave the way for a
more efficient, time-saving, and accessible landscape in legal research,
benefiting legal professionals, academics, and researchers.

我们提出了一种结合自然语言处理和机器学习技术的方法，通过创建文本嵌入来增强法律案例描述的组织和利用，实现无监督聚类和有监督引文检索，以自动化提取引文的过程，从而实现更高效、节省时间、便捷的法律研究和论证。

利用上下文相似性进行判决引用检索

Judgement Citation Retrieval using Contextual Similarity

Legal research is a crucial task in the practice of law. It requires intense
human effort and intellectual prudence to research a legal case and prepare
arguments. Recent boom in generative AI has not translated to proportionate
rise in impactful legal applications, because of low trustworthiness and and
the scarcity of specialized datasets for training Large Language Models (LLMs).
This position paper explores the potential of LLMs within Legal Text Analytics
(LTA), highlighting specific areas where the integration of human expertise can
significantly enhance their performance to match that of experts. We introduce
a novel dataset and describe a human centered, compound AI system that
principally incorporates human inputs for performing LTA tasks with LLMs.

法律研究是法律实践中至关重要的任务，该研究论文探讨了在法律文本分析中，结合人类专业知识提高大型语言模型（LLMs）性能的潜力，并介绍了一种新的数据集和基于人类输入的复合人工智能系统。

面向人类的印度法律文本分析的人工智能

Human Centered AI for Indian Legal Text Analytics

Studies have shown that some Natural Language Processing (NLP) systems encode
and replicate harmful biases with potential adverse ethical effects in our
society. In this article, we propose an approach for identifying gender and
racial stereotypes in word embeddings trained on judicial opinions from U.S.
case law. Embeddings containing stereotype information may cause harm when used
by downstream systems for classification, information extraction, question
answering, or other machine learning systems used to build legal research
tools. We first explain how previously proposed methods for identifying these
biases are not well suited for use with word embeddings trained on legal
opinion text. We then propose a domain adapted method for identifying gender
and racial biases in the legal domain. Our analyses using these methods suggest
that racial and gender biases are encoded into word embeddings trained on legal
opinions. These biases are not mitigated by exclusion of historical data, and
appear across multiple large topical areas of the law. Implications for
downstream systems that use legal opinion word embeddings and suggestions for
potential mitigation strategies based on our observations are also discussed.

本文针对自然语言处理中的语言偏见问题，提出了一种用于识别美国司法审判文件中的嵌入有性别和种族刻板印象的词语的方法，并对法律域中的这些性别和种族偏见问题进行了分析并提出了可能的缓解策略。