The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solution for legal text simplification, evaluating their true capabilities requires a multifaceted approach that goes beyond surface-level metrics. This paper introduces a comprehensive dual-aspect evaluation framework to address this need. First, we establish a performance benchmark for four state-of-the-art large language models (GPT-4o, Claude 3 Opus, Gemini 1.5 Pro, and Grok-1) across three key dimensions: Accuracy, Readability, and Consistency. Second, to understand the "why" behind these performance scores, we conduct a large-scale error analysis on a curated dataset of 60 complex Vietnamese legal articles, using a novel, expert-validated error typology. Our results reveal a crucial trade-off: models like Grok-1 excel in Readability and Consistency but compromise on fine-grained legal Accuracy, while models like Claude 3 Opus achieve high Accuracy scores that mask a significant number of subtle but critical reasoning errors. The error analysis pinpoints \textit{Incorrect Example} and \textit{Misinterpretation} as the most prevalent failures, confirming that the primary challenge for current LLMs is not summarization but controlled, accurate legal reasoning. By integrating a quantitative benchmark with a qualitative deep dive, our work provides a holistic and actionable assessment of LLMs for legal applications.

翻译：越南法律文本的复杂性构成了公众获取司法服务的重大障碍。尽管大语言模型为法律文本简化提供了有前景的解决方案，但评估其真实能力需要超越表面指标的多方面方法。本文提出了一种全面的双方面评估框架以应对这一需求。首先，我们为四种最先进的大语言模型（GPT-4o、Claude 3 Opus、Gemini 1.5 Pro和Grok-1）在三个关键维度——准确性、可读性和一致性——上建立了性能基准。其次，为理解这些性能分数背后的“原因”，我们基于一个包含60篇复杂越南法律文章的精编数据集，采用一种新颖的、经专家验证的错误类型学，开展了大规模错误分析。我们的结果揭示了一个关键权衡：像Grok-1这样的模型在可读性和一致性上表现优异，但牺牲了细粒度的法律准确性；而像Claude 3 Opus这样的模型虽取得了高准确率分数，却掩盖了大量细微但关键性的推理错误。错误分析精准定位了\textit{错误示例}和\textit{误解}作为最普遍的失败类型，证实了当前大语言模型的主要挑战并非摘要生成，而是受控且准确的法律推理。通过将定量基准与定性深度分析相结合，我们的工作为法律应用中的大语言模型提供了整体且可操作的评估。