VLegal-Bench: Cognitively Grounded Benchmark for Vietnamese Legal Reasoning of Large Language Models
- URL: http://arxiv.org/abs/2512.14554v4
- Date: Wed, 24 Dec 2025 02:46:01 GMT
- Title: VLegal-Bench: Cognitively Grounded Benchmark for Vietnamese Legal Reasoning of Large Language Models
- Authors: Nguyen Tien Dong, Minh-Anh Nguyen, Thanh Dat Hoang, Nguyen Tuan Ngoc, Dao Xuan Quang Minh, Phan Phi Hai, Nguyen Thi Ngoc Anh, Dang Van Tu, Binh Vu,
- Abstract summary: The Vietnamese Legal Benchmark (VLegal-Bench) is the first benchmark designed to assess large language models (LLMs) on Vietnamese legal tasks.<n>The benchmark comprises 10,450 samples generated through a rigorous annotation pipeline.<n>By providing a standardized, transparent, and cognitively informed evaluation framework, VLegal-Bench establishes a solid foundation for assessing LLM performance in Vietnamese legal contexts.
- Score: 0.4310799044841232
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The rapid advancement of large language models (LLMs) has enabled new possibilities for applying artificial intelligence within the legal domain. Nonetheless, the complexity, hierarchical organization, and frequent revisions of Vietnamese legislation pose considerable challenges for evaluating how well these models interpret and utilize legal knowledge. To address this gap, the Vietnamese Legal Benchmark (VLegal-Bench) is introduced, the first comprehensive benchmark designed to systematically assess LLMs on Vietnamese legal tasks. Informed by Bloom's cognitive taxonomy, VLegal-Bench encompasses multiple levels of legal understanding through tasks designed to reflect practical usage scenarios. The benchmark comprises 10,450 samples generated through a rigorous annotation pipeline, where legal experts label and cross-validate each instance using our annotation system to ensure every sample is grounded in authoritative legal documents and mirrors real-world legal assistant workflows, including general legal questions and answers, retrieval-augmented generation, multi-step reasoning, and scenario-based problem solving tailored to Vietnamese law. By providing a standardized, transparent, and cognitively informed evaluation framework, VLegal-Bench establishes a solid foundation for assessing LLM performance in Vietnamese legal contexts and supports the development of more reliable, interpretable, and ethically aligned AI-assisted legal systems. To facilitate access and reproducibility, we provide a public landing page for this benchmark at https://vilegalbench.cmcai.vn/.
Related papers
- LegalOne: A Family of Foundation Models for Reliable Legal Reasoning [54.57434222018289]
We present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain.<n>LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning.<n>We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI.
arXiv Detail & Related papers (2026-01-31T10:18:32Z) - PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice [67.71760070255425]
We introduce PLawBench, a practical benchmark for evaluating large language models (LLMs) in legal practice scenarios.<n>PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics.<n>Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs.
arXiv Detail & Related papers (2026-01-23T11:36:10Z) - LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence [74.05988707492058]
Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making.<n>Existing benchmarks are result-oriented and fail to systematically evaluate the legal intelligence of large language models (LLMs)<n>We propose LexGenius, an expert-level Chinese legal benchmark for evaluating legal GI in LLMs.
arXiv Detail & Related papers (2025-12-04T08:48:02Z) - Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts [54.15982476754607]
Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks.<n>This study defines complicit facilitation as the provision of guidance or support that enables illicit user instructions.<n>Using real-world legal cases and established legal frameworks, we construct an evaluation benchmark spanning 269 illicit scenarios and 50 illicit intents.
arXiv Detail & Related papers (2025-11-25T16:01:31Z) - GLARE: Agentic Reasoning for Legal Judgment Prediction [60.13483016810707]
Legal judgment prediction (LJP) has become increasingly important in the legal field.<n>Existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge.<n>We introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules.
arXiv Detail & Related papers (2025-08-22T13:38:12Z) - LLMs for Legal Subsumption in German Employment Contracts [3.3916160303055567]
This study explores the use of Large Language Models and in-context learning to evaluate the legality of clauses in German employment contracts.<n>Our work evaluates the ability of different LLMs to classify clauses as "valid," "unfair," or "void" under three legal context variants.<n>Results show that full-text sources moderately improve performance, while examination guidelines significantly enhance recall for void clauses and weighted F1-Score, reaching 80%.
arXiv Detail & Related papers (2025-07-02T14:07:54Z) - LegalBench.PT: A Benchmark for Portuguese Law [17.554201334646056]
We present LegalBench.PT, the first comprehensive legal benchmark covering key areas of Portuguese law.<n>We first collect long-form questions and answers from real law exams, and then use GPT-4o to convert them into multiple-choice, true/false, and matching formats.
arXiv Detail & Related papers (2025-02-22T21:07:12Z) - LegalAgentBench: Evaluating LLM Agents in Legal Domain [53.70993264644004]
LegalAgentBench is a benchmark specifically designed to evaluate LLM Agents in the Chinese legal domain.<n>LegalAgentBench includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.
arXiv Detail & Related papers (2024-12-23T04:02:46Z) - Legal Evalutions and Challenges of Large Language Models [42.51294752406578]
We use the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions.
We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain.
arXiv Detail & Related papers (2024-11-15T12:23:12Z) - Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models [7.797885529152412]
Large language models (LLMs) have demonstrated remarkable performance in the legal domain.
However their efficacy remains limited for non-standardized tasks and tasks in languages other than English.
This underscores the need for careful evaluation of LLMs within each legal system before application.
arXiv Detail & Related papers (2024-10-11T11:41:02Z) - InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws.
We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries.
InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
arXiv Detail & Related papers (2024-06-21T06:19:03Z) - Automating IRAC Analysis in Malaysian Contract Law using a Semi-Structured Knowledge Base [22.740895683854568]
This paper introduces LegalSemi, a benchmark specifically curated for legal scenario analysis.<n>LegalSemi comprises 54 legal scenarios, each rigorously annotated by legal experts, based on the comprehensive IRAC (Issue, Rule, Application, Conclusion) framework from Malaysian Contract Law.<n>A series of experiments were conducted to assess the usefulness of LegalSemi for IRAC analysis.
arXiv Detail & Related papers (2024-06-19T04:59:09Z) - LAiW: A Chinese Legal Large Language Models Benchmark [17.66376880475554]
General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI.
We are the first to build the Chinese legal LLMs benchmark LAiW, based on the logic of legal practice.
arXiv Detail & Related papers (2023-10-09T11:19:55Z) - LexGLUE: A Benchmark Dataset for Legal Language Understanding in English [15.026117429782996]
We introduce the Legal General Language Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks.
We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
arXiv Detail & Related papers (2021-10-03T10:50:51Z) - Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding.
We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
arXiv Detail & Related papers (2021-05-09T09:39:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.