Abstract
Large-language models (LLMs) have been shown to respond in a variety of ways for classification tasks outside of question-answering. LLM responses are sometimes called "hallucinations" since the output is not what is ex pected. Memorization strategies in LLMs are being studied in detail, with the goal of understanding how LLMs respond. We perform a deep dive into a classification task based on United States Supreme Court (SCOTUS) decisions. The SCOTUS corpus is an ideal classification task to study for LLM memory accuracy because it presents significant challenges due to extensive sentence length, complex legal terminology, non-standard structure, and domain-specific vocabulary. Experimentation is performed with the latest LLM fine tuning and retrieval-based approaches, such as parameter-efficient fine-tuning, auto-modeling, and others, on two traditional category-based SCOTUS classification tasks: one with 15 labeled topics and another with 279. We show that prompt-based models with memories, such as DeepSeek, can be more robust than previous BERT-based models on both tasks scoring about 2 points better than previous models not based on prompting.
Abstract (translated)
大型语言模型（LLMs）已被证明在问答之外的分类任务中会以多种方式响应，有时这些响应被称为“幻觉”，因为输出结果不符合预期。对于LLM中的记忆策略研究正在深入进行，目的是理解这些模型如何做出反应。我们针对美国最高法院（SCOTUS）裁决开展了一项基于分类任务的深度分析。由于句子长度长、法律术语复杂、结构非标准化以及专业词汇的存在，SCOTUS语料库成为了研究LLM记忆准确性的一个理想分类任务。 实验采用了最新的参数高效微调和检索式方法，例如参数高效的微调、自动建模等技术，在两个基于传统类别划分的SCOTUS分类任务上进行测试：一个是包含15个主题标签的任务，另一个则包含了279个。我们展示了带有记忆功能的提示驱动模型（如DeepSeek）在两项任务中的表现均优于先前的BERT基线模型，得分大约高出2分。 通过这种方法的研究表明，具有记忆机制和基于提示调整能力的大型语言模型能够更好地完成复杂法律文本分类任务，并且相较于传统的仅依赖微调的方法，这些新方法可以有效提升模型的表现。
URL
https://arxiv.org/abs/2512.13654
