金融文本专用嵌入模型，比通用模型召回率高23.6个百分点。
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt
- 基于1430万条金融问答对微调，专攻金融术语与长尾表达
- 在测试集上召回率62.8%，超越最强通用模型23.6个百分点
- 特别擅长处理含公司名、时间点的复杂金融查询，适合量化研究者
金融文档充斥着专业术语、晦涩行话和奇特缩写，对通用文本嵌入构成挑战。但目前鲜有金融专用嵌入模型发表，部分原因在于缺乏公开数据集与基准。我们提出BAM嵌入，基于精心构建的1430万条查询-段落对进行微调。实验证明领域特化训练的优势：BAM在保留测试集上的召回率（Recall@1）达62.8%，而最佳通用嵌入（OpenAI）仅为39.2%。此外，BAM在FinanceBench上将问答准确率提升8%，对包含公司名、日期及前瞻性表述的金融特异性查询表现出更强敏感性。为推动后续研究，本文详细描述方法，量化硬负样本挖掘与数据规模的重要性。
原文摘要 · Abstract (English)
Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. Yet, few text embeddings specialized for finance have been reported in the literature, perhaps in part due to a lack of public datasets and benchmarks. We present BAM embeddings, a set of text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs. Demonstrating the benefits of domain-specific training, BAM embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embedding from OpenAI. Further, BAM embeddings increase question answering accuracy by 8% on FinanceBench and show increased sensitivity to the finance-specific elements that are found in detailed, forward-looking and company and date-specific queries. To support further research we describe our approach in detail, quantify the importance of hard negative mining and dataset scale.
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