LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text

Abstract: As LLMs are increasingly used in legal applications, current evaluation benchmarks tend to focus mainly on factual accuracy while largely neglecting important linguistic quality aspects such as clarity, coherence, and terminology. To address this gap, we propose three steps: First, we develop a regression model to evaluate the quality of legal texts based on clarity, coherence, and terminology. Second, we create a specialized set of legal questions. Third, we analyze 49 LLMs using this evaluation framework. Our analysis identifies three key findings: First, model quality levels off at 14 billion parameters, with only a marginal improvement of $2.7\%$ noted at 72 billion parameters. Second, engineering choices such as quantization and context length have a negligible impact, as indicated by statistical significance thresholds above 0.016. Third, reasoning models consistently outperform base architectures. A significant outcome of our research is the release of a ranking list and Pareto analysis, which highlight the Qwen3 series as the optimal choice for cost-performance tradeoffs. This work not only establishes standardized evaluation protocols for legal LLMs but also uncovers fundamental limitations in current training data refinement approaches. Code and models are available at: https://github.com/lyxx3rd/LegalEval-Q.

LegalEval-Q: Benchmarking Text Quality in LLM-generated Legal Texts

The paper "LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text" addresses a critical void in the assessment of text generated by large language models (LLMs) deployed in legal applications. Notably, existing evaluation benchmarks often emphasize factual accuracy but lack comprehensive examination of other significant linguistic attributes such as clarity, coherence, and terminological precision. This research makes a substantial contribution by proposing a multidimensional assessment framework tailored to evaluate these neglected aspects in legal texts, thereby addressing practical challenges in model selection and optimization within the legal domain.

Principal Contributions

The authors present three primary innovations in their study:

1. Development of a Specialized Benchmark: A regression model was crafted to evaluate legal text quality across dimensions of clarity, coherence, and terminological precision. This provides a standardized metric for assessing the nuanced attributes of legal text quality that are often overlooked in traditional benchmarks.

2. Construction of a Legal Questions Dataset: A comprehensive validation set of legal queries was curated, spanning subdomains of criminal law, civil code, and general statutes. This dataset enables rigorous empirical analysis and evaluation of LLM-generated responses within varied legal contexts.

3. Empirical Analysis and Model Comparisons: The study systematically analyzes 49 LLMs using the proposed evaluation framework. It elucidates two key observations on model performance—firstly, a plateau in performance gains at model sizes exceeding 14 billion parameters, and secondly, the negligible impact of engineering choices such as model quantization and context length on the text quality when statistical significance levels exceed 0.016.

Key Findings

The study's comprehensive analysis yields insightful findings applicable to both theoretical understanding and practical deployment considerations of LLMs in legal contexts. Particularly:

• Scale vs. Quality Saturation: Textual quality improvements plateau around 14 billion parameters, with only minimal gains observed at larger scales. This challenges prevalent perceptions about scalability in model performance, urging a reconsideration of efficient architecture design over mere parameter escalation.

• Impact of Engineering Choices: Choices such as model quantization and extended context lengths do not significantly affect the quality of generated texts at statistically rigorous levels (p > 0.016). This indicates that efforts toward optimizing computational efficiency and minimizing deployment costs can be prioritized without compromising output quality.

• Superiority of Reasoning Models: Models optimized for reasoning capabilities consistently outperform their base counterparts, indicating the substantial benefits of fine-tuning strategies that enhance domain-specific reasoning skills.

Implications and Future Directions

The paper not only sets a precedent for standardized evaluation protocols in the legal domain but also highlights inherent limitations in current training data refinements that extend beyond simple parameter expansion. It presents practitioners and researchers with actionable insights into selecting LLMs for legal applications by employing Pareto analysis to navigate the cost-performance landscape effectively.

Future research avenues proposed include the expansion of the framework for cross-domain applicability, incorporating dynamic scoring activations to overcome current ceiling limitations, and establishing industry-wide benchmarks for multidimensional text quality evaluations. These developments would significantly bolster the methodological foundation of domain-specific linguistic assessments and enhance the practical deployment of AI systems in specialized fields such as law.

In summary, "LegalEval-Q" delivers significant advancements in understanding and analyzing the textual quality of LLM outputs in legal contexts, providing the necessary tools for evaluative precision and clarity that are pivotal in such high-stakes domains.