With the rise of large language models (LLMs), our exposure to benchmarks — not to mention the sheer number and variety of them — has surged. Given the opaque nature of LLMs and other AI systems, benchmarks have become the standard way to compare their performance.
These are standardized tests or data sets that evaluate how well models perform on specific tasks. As a result, every new model release brings updated leaderboard results, and embedding models are no exception.
Today, embeddings power the search layer of AI applications, yet…
