Encoding legislative text in a formal representation is an important
prerequisite to different tasks in the field of AI & Law. For example,
rule-based expert systems focused on legislation can support laypeople in
understanding how legislation applies to them and provide them with helpful
context and information. However, the process of analyzing legislation and
other sources to encode it in the desired formal representation can be
time-consuming and represents a bottleneck in the development of such systems.
Here, we investigate to what degree large language models (LLMs), such as
GPT-4, are able to automatically extract structured representations from
legislation. We use LLMs to create pathways from legislation, according to the
JusticeBot methodology for legal decision support systems, evaluate the
pathways and compare them to manually created pathways. The results are
promising, with 60% of generated pathways being rated as equivalent or better
than manually created ones in a blind comparison. The approach suggests a
promising path to leverage the capabilities of LLMs to ease the costly
development of systems based on symbolic approaches that are transparent and
explainable.

利用大型语言模型自动提取立法文件的结构化表示，为发展透明且可解释的基于符号方法的系统提供了一条有前景的途径。

从文本到结构：利用大型语言模型支持法律专家系统的发展

From Text to Structure: Using Large Language Models to Support the  Development of Legal Expert Systems

Laypeople (i.e. individuals without legal training) may often have trouble
resolving their legal problems. In this work, we present the JusticeBot
methodology. This methodology can be used to build legal decision support
tools, that support laypeople in exploring their legal rights in certain
situations, using a hybrid case-based and rule-based reasoning approach. The
system ask the user questions regarding their situation and provides them with
legal information, references to previous similar cases and possible next
steps. This information could potentially help the user resolve their issue,
e.g. by settling their case or enforcing their rights in court. We present the
methodology for building such tools, which consists of discovering typically
applied legal rules from legislation and case law, and encoding previous cases
to support the user. We also present an interface to build tools using this
methodology and a case study of the first deployed JusticeBot version, focused
on landlord-tenant disputes, which has been used by thousands of individuals.

通过 JusticeBot 方法论以混合案例和规则推理的方式构建法律支持工具，帮助法律知识有限的普通人解决其问题，特别是关于房东和租户纠纷的案例研究。