The effectiveness of Large Language Models (LLMs) in legal reasoning is often
limited due to the unique legal terminologies and the necessity for highly
specialized knowledge. These limitations highlight the need for high-quality
data tailored for complex legal reasoning tasks. This paper introduces
LEGALSEMI, a benchmark specifically curated for legal scenario analysis.
LEGALSEMI comprises 54 legal scenarios, each rigorously annotated by legal
experts, based on the comprehensive IRAC (Issue, Rule, Application, Conclusion)
framework. In addition, LEGALSEMI is accompanied by a structured knowledge
graph (SKG). A series of experiments were conducted to assess the usefulness of
LEGALSEMI for IRAC analysis. The experimental results demonstrate the
effectiveness of incorporating the SKG for issue identification, rule
retrieval, application and conclusion generation using four different LLMs.
LEGALSEMI will be publicly available upon acceptance of this paper.

LEGALSEMI 是一个专门为法律情境分析而制作的基准库，包含 54 个经过法律专家严格注释的法律情境，每个情境都基于完整的 IRAC 框架（问题、规则、应用、结论），并伴随着一个结构化知识图谱（SKG）。通过对四种不同的 LLM 进行一系列实验，实验结果显示了 LEGALSEMI 在问题识别、规则检索、应用和结论生成等方面的有效性。

桥 接 法律 与 数据：通过 IRAC 方法增强 推理的 半结构化 数据 集

Bridging Law and Data: Augmenting Reasoning via a Semi-Structured  Dataset with IRAC methodology

Few-shot learning aims to learn novel categories from very few samples given
some base categories with sufficient training samples. The main challenge of
this task is the novel categories are prone to dominated by color, texture,
shape of the object or background context (namely specificity), which are
distinct for the given few training samples but not common for the
corresponding categories (see Figure 1). Fortunately, we find that transferring
information of the correlated based categories can help learn the novel
concepts and thus avoid the novel concept being dominated by the specificity.
Besides, incorporating semantic correlations among different categories can
effectively regularize this information transfer. In this work, we represent
the semantic correlations in the form of structured knowledge graph and
integrate this graph into deep neural networks to promote few-shot learning by
a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing
each node with the classifier weight of the corresponding category, a
propagation mechanism is learned to adaptively propagate node message through
the graph to explore node interaction and transfer classifier information of
the base categories to those of the novel ones. Extensive experiments on the
ImageNet dataset show significant performance improvement compared with current
leading competitors. Furthermore, we construct an ImageNet-6K dataset that
covers larger scale categories, i.e, 6,000 categories, and experiments on this
dataset further demonstrate the effectiveness of our proposed model. Our codes
and models are available at this https URL .

该研究提出了一种基于知识图谱的网络模型，通过在少量训练数据的基础上进行信息传递和语义相关性分析，实现在大规模数据集上进行 few-shot 的学习，取得了较好的效果。