Introducing RTEB: A New Standard for Retrieval Evaluation

TL;DR – We’re excited to introduce the beta version of the Retrieval Embedding Benchmark (RTEB) , a new benchmark designed to reliably evaluate the retrieval accuracy of embedding models for real-world applications. Existing benchmarks struggle to measure true generalization, while RTEB addresses this with a hybrid strategy of open and private datasets. Its goal is simple: to create a fair, transparent, and application-focused standard for measuring how models perform on data they haven’t seen before.

The performance of many AI applications, from RAG and agents to recommendation systems, is fundamentally limited by the quality of search and retrieval. As such, accurately measuring the retrieval quality of embedding models is a common pain point for developers. How do you really know how well a model will perform in the wild?

This is where things get tricky. The current standard for evaluation often relies on a model's "zero-shot" performance on public benchmarks. However, this is, at best, an approximation of a model's true generalization capabilities. When models are repeatedly evaluated against the same public datasets, a gap emerges between their reported scores and their actual performance on new, unseen data.

Misalignment with Today’s AI Applications . Many benchmarks are poorly aligned with the enterprise use cases that developers are building today. They often rely on academic datasets or on retrieval tasks derived from QA datasets, which, while useful in their own right, were not designed to evaluate retrieval and can fail to capture the distributional biases and complexities encountered in real-world retrieval scenarios. Benchmarks which do not possess these issues are often too narrow, focusing on a single domain like code retrieval, making them unsuitable for evaluating general-purpose models.

RTEB is designed with a particular emphasis on enterprise use cases. Instead of a complex hierarchy, it uses simple groups for clarity. A single dataset can belong to multiple groups (e.g., a German law dataset exists in both the "law" and "German" groups).

The RTEB leaderboard is available today on Hugging Face as a part of the new Retrieval section on the MTEB leaderboard. We invite you to check it out, evaluate your models, and join us in building a better, more reliable benchmark for the entire AI community.

To address these challenges, we developed RTEB, a benchmark built to provide a reliable standard for evaluating retrieval models.

While the underlying evaluation methodology and metrics (such as NDCG@10) are well-known and robust, the integrity of existing benchmarks is often set back by the following issues:

The Generalization Gap . The current benchmark ecosystem inadvertently encourages "teaching to the test." When training data sources overlap with evaluation datasets, a model's score can become inflated, undermining a benchmark's integrity. This practice, whether intentional or not, is evident in the training datasets of several models. This creates a feedback loop where models are rewarded for memorizing test data rather than developing robust, generalizable capabilities.