FinMTEB: Finance Massive Text Embedding Benchmark
Authors: name: Yixuan Tang, name: Yi Yang
Organization: FinMTEB

Embedding models play a crucial role in representing and retrieving
information across various NLP applications. Recent advances in large language
models (LLMs) have further enhanced the performance of embedding models. While
these models are often benchmarked on general-purpose datasets, real-world
applications demand domain-specific evaluation. In this work, we introduce the
Finance Massive Text Embedding Benchmark (FinMTEB), a specialized counterpart
to MTEB designed for the financial domain. FinMTEB comprises 64 financial
domain-specific embedding datasets across 7 tasks that cover diverse textual
types in both Chinese and English, such as financial news articles, corporate
annual reports, ESG reports, regulatory filings, and earnings call transcripts.
We also develop a finance-adapted model, FinPersona-E5, using a persona-based
data synthetic method to cover diverse financial embedding tasks for training.
Through extensive evaluation of 15 embedding models, including FinPersona-E5,
we show three key findings: (1) performance on general-purpose benchmarks shows
limited correlation with financial domain tasks; (2) domain-adapted models
consistently outperform their general-purpose counterparts; and (3)
surprisingly, a simple Bag-of-Words (BoW) approach outperforms sophisticated
dense embeddings in financial Semantic Textual Similarity (STS) tasks,
underscoring current limitations in dense embedding techniques. Our work
establishes a robust evaluation framework for financial NLP applications and
provides crucial insights for developing domain-specific embedding models.