When deploying LLMs for legal tasks, don't rely on overall accuracy scores alone—detailed error analysis shows models make subtle but critical reasoning mistakes that surface-level metrics miss, especially with complex domain-specific language.
This paper evaluates four leading LLMs (GPT-4o, Claude 3 Opus, Gemini 1.5 Pro, Grok-1) on Vietnamese legal text simplification using both quantitative benchmarks and detailed error analysis. The study reveals that models face a trade-off between readability and legal accuracy, with the main challenge being precise legal reasoning rather than summarization.