发表机构
JudicialMind（司法思维机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出参数0.6B的GreenLeaf Law Embed Tiny法律领域检索嵌入模型，通过两阶段训练等方法，在相关基准取得优异成绩，可部署于资源受限环境。
AI 中文摘要
我们提出GreenLeaf Law Embed Tiny，这是一款拥有0.6B参数的法律领域检索嵌入模型。GreenLeaf-Tiny在大规模法律嵌入基准（Massive Legal Embedding Benchmark, MLEB）上取得75.11%的成绩，在MTEB(Law, v1)上取得64.38%的成绩，在参数小于1B的模型中展现出有竞争力的性能。我们的方法结合了两阶段训练流程：首先将较大的教师模型的知识蒸馏到紧凑的学生架构中，随后利用难负例挖掘进行领域特定微调；我们还使用了精心整理的数据集，包含340万条查询-段落对，其中涵盖15万条来自不同司法管辖区的人工整理样本；此外，我们采用了支持多种量化级别（BF16、INT8、二进制）的高效推理架构，可在资源受限的环境中部署。我们对训练方法、架构选择以及法律检索任务上的综合评估进行了详细分析，结果表明，利用高质量数据进行领域特定训练可提升专业领域应用的性能。
英文摘要
We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and 64.38% on MTEB(Law, v1),demonstrating competitive performance among models under 1B parameters. Our approach combines a two-stage training pipeline that first distills knowledge from a larger teacher model into a compact student architecture, then applies domain-specific fine-tuning with hard negative mining; a carefully curated dataset of 3.4 million query-passage pairs, including 150,000 human-curated samples across diverse legal jurisdictions; and an efficient inference architecture supporting multiple quantization levels (BF16, INT8, binary) enabling deployment in resource-constrained environments. We provide detailed analysis of our training methodology, architectural choices, and comprehensive evaluation across legal retrieval tasks. Our results demonstrate that domain-specific training with high-quality data can improve performance for specialized domain applications
Comments7 pages, 2 figures, IEEE dual-column format. Submitted to arXiv for preprint distribution
