From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text

Researchers evaluated four major LLMs (GPT-4o, Claude 3 Opus, Gemini 1.5 Pro, Grok-1) on Vietnamese legal text simplification using a dual-aspect framework combining benchmarking metrics with expert-validated error analysis. The study reveals a critical trade-off: while some models excel at readability, they sacrifice legal accuracy, and high accuracy scores often mask subtle but serious reasoning errors that matter in legal contexts.

This research addresses a practical gap in LLM evaluation methodology by moving beyond surface-level metrics to expose the reasoning quality behind benchmark scores. Vietnam's complex legal landscape creates genuine demand for AI-assisted text simplification, yet existing evaluation frameworks fail to capture whether models truly understand legal nuance or merely produce plausible-sounding outputs. The dual-aspect approach—combining quantitative benchmarks with qualitative error typology—represents a more rigorous evaluation standard applicable beyond Vietnamese law.

The findings highlight a fundamental challenge in deploying LLMs for high-stakes applications. Claude 3 Opus achieving high accuracy scores while harboring critical reasoning errors demonstrates that aggregate metrics can be misleading. The identification of "Incorrect Example" and "Misinterpretation" as primary failure modes suggests current models struggle with the constrained reasoning legal domains demand, not summarization capabilities. This distinction matters significantly for practitioners considering LLM deployment.

For the AI industry, this work establishes a template for specialized domain evaluation. Legal applications represent a high-value, regulated use case where LLM limitations pose real risks—incorrect legal interpretations could harm individuals seeking justice through AI-mediated access. The trade-off between readability and accuracy signals that different models may require different applications: Grok-1 for accessible explanation, Claude 3 Opus for accuracy-critical tasks with human review.

Looking ahead, the field should expect more granular error taxonomies emerging for specialized domains. This research suggests that future LLM procurement decisions in legal, medical, and financial sectors will increasingly require domain-specific evaluation protocols rather than relying on general-purpose benchmarks.

• →Claude 3 Opus achieves high accuracy scores while containing subtle reasoning errors invisible to traditional metrics.
• →Current LLMs struggle with controlled legal reasoning rather than text simplification or summarization tasks.
• →A novel error typology reveals that Incorrect Example and Misinterpretation are the dominant failure modes for legal text.
• →Grok-1 excels in readability but sacrifices fine-grained legal accuracy, creating a performance trade-off pattern.
• →Dual-aspect evaluation frameworks combining quantitative and qualitative analysis provide more actionable insights than benchmark scores alone.