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

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A study introduces a dual-aspect evaluation framework for Large Language Models (LLMs) on complex Vietnamese legal texts, aiming to improve public access to justice. The research benchmarks four state-of-the-art LLMs (GPT-4o, Claude 3 Opus, Gemini 1.5 Pro, and Grok-1) across Accuracy, Readability, and Consistency using a dataset of 60 complex Vietnamese legal articles. It also conducts a large-scale error analysis with an expert-validated nine-category typology. Findings indicate a trade-off: Grok-1 excels in Readability and Consistency but shows weaknesses in fine-grained legal Accuracy, while Claude 3 Opus achieves high Accuracy but masks significant reasoning errors, particularly Misinterpretation. The analysis identifies "Incorrect Example" and "Misinterpretation" as the most prevalent failures, highlighting that LLMs struggle more with controlled legal reasoning than with summarization.

Key takeaway

For research scientists developing legal AI, you should prioritize diagnostic evaluation frameworks that combine quantitative benchmarks with qualitative error analysis. Relying solely on aggregate accuracy scores can mask critical reasoning failures like misinterpretation or oversimplification, which are particularly risky in high-stakes legal contexts. Implement a risk-aware human-in-the-loop system, leveraging error typologies to target oversight on specific failure modes, especially for generative tasks like example creation.

Key insights

LLMs struggle with legal reasoning and application, often masking critical errors despite high surface-level accuracy.

Principles

• Evaluation needs both quantitative and qualitative aspects.
• High linguistic competence can mask reasoning deficits.
• "Alignment tax" may affect model performance on specific tasks.

Method

A dual-aspect framework combines large-scale performance benchmarking (Accuracy, Readability, Consistency) with an in-depth, expert-validated, nine-category error typology applied to 60 complex Vietnamese legal articles.

In practice

• Flag inference-heavy model outputs for interpretation review.
• Route example generation to legal professionals.
• Use error typologies for risk-aware human oversight.

Topics

• Vietnamese Legal Domain
• Legal Text Simplification
• LLM Performance Benchmarking
• Legal Error Typology
• LLM Reasoning Failures

Best for: Research Scientist, AI Scientist, NLP Engineer, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.