Developing domain models is one of the few remaining places that require
manual human labor in AI planning. Thus, in order to make planning more
accessible, it is desirable to automate the process of domain model generation.
To this end, we investigate if large language models (LLMs) can be used to
generate planning domain models from simple textual descriptions. Specifically,
we introduce a framework for automated evaluation of LLM-generated domains by
comparing the sets of plans for domain instances. Finally, we perform an
empirical analysis of 7 large language models, including coding and chat models
across 9 different planning domains, and under three classes of natural
language domain descriptions. Our results indicate that LLMs, particularly
those with high parameter counts, exhibit a moderate level of proficiency in
generating correct planning domains from natural language descriptions. Our
code is available at this https URL

通过对 7 个大型语言模型进行实证分析，我们发现大型语言模型在从自然语言描述中生成正确的规划领域模型方面具有适中的能力。

大型语言模型作为规划领域生成器

Large Language Models as Planning Domain Generators

Designing domain models and software architectures represents a significant
challenge in software development, as the resulting architectures play a vital
role in fulfilling the system's quality of service. Due to time pressure,
architects often model only one architecture based on their known limited
domain understanding, patterns, and experience instead of thoroughly analyzing
the domain and evaluating multiple candidates, selecting the best fitting.
Existing approaches try to generate domain models based on requirements, but
still require time-consuming manual effort to achieve good results. Therefore,
in this vision paper, we propose a method to generate software architecture
candidates semi-automatically based on requirements using artificial
intelligence techniques. We further envision an automatic evaluation and
trade-off analysis of the generated architecture candidates using, e.g., the
architecture trade-off analysis method combined with large language models and
quantitative analyses. To evaluate this approach, we aim to analyze the quality
of the generated architecture models and the efficiency and effectiveness of
our proposed process by conducting qualitative studies.

使用人工智能技术基于需求半自动生成软件架构候选方案，并通过定量分析和架构权衡分析方法进行自动评估和交叉分析，以提高生成架构模型的质量、效率和有效性。

由需求到体系结构：基于人工智能的半自动软件体系结构生成之旅

From Requirements to Architecture: An AI-Based Journey to  Semi-Automatically Generate Software Architectures

Legal Judgment Prediction (LJP) has become an increasingly crucial task in
Legal AI, i.e., predicting the judgment of the case in terms of case fact
description. Precedents are the previous legal cases with similar facts, which
are the basis for the judgment of the subsequent case in national legal
systems. Thus, it is worthwhile to explore the utilization of precedents in the
LJP. Recent advances in deep learning have enabled a variety of techniques to
be used to solve the LJP task. These can be broken down into two categories:
large language models (LLMs) and domain-specific models. LLMs are capable of
interpreting and generating complex natural language, while domain models are
efficient in learning task-specific information. In this paper, we propose the
precedent-enhanced LJP framework (PLJP), a system that leverages the strength
of both LLM and domain models in the context of precedents. Specifically, the
domain models are designed to provide candidate labels and find the proper
precedents efficiently, and the large models will make the final prediction
with an in-context precedents comprehension. Experiments on the real-world
dataset demonstrate the effectiveness of our PLJP. Moreover, our work shows a
promising direction for LLM and domain-model collaboration that can be
generalized to other vertical domains.

利用先例加强的 LJP 框架（PLJP）结合了大型语言模型和领域模型的优势，能够有效地预测案件的判决结果，并展示了 LLM 和领域模型协作的有希望的方向。

利用 LLM 与领域模型合作增强的法律判决预测

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model  Collaboration

In this article we analyse the notion of knowledge role. First of all, we
present how the relationship between problem solving methods and domain models
is tackled in different approaches. We concentrate on how they cope with this
issue in the knowledge engineering process. Secondly, we introduce several
properties which can be used to analyse, characterise and define the notion of
role. We evaluate and compare the works exposed previously following these
dimensions. This analysis suggests some developments to better exploit the
relationship between reasoning and domain knowledge. We present them in a last
section.

本文主要探讨知识角色的概念，介绍了问题解决方法和领域模型之间的关系，并分析了这些角色的属性，提出了更好地利用推理和领域知识之间关系的发展方向。