Re:What do MTEB scores mean, and how should you choose an em...
by svcrunch on Saturday October 10, 2026 @02:14PM
(#50035586)
Thanks for sharing this writeup.
At my previous company, I led the development of a retrieval embedding model called Boomerang, which focused on minimizing same-language bias. Briefly, the way most embedders are trained, they will prefer worse answers in the language that the question was asked, versus better answers in a different language. I call that same-language bias.
Anyway, an investor at a16z was quizzing me on why this particular model wasn't top-ranked on MTEB. I tried explaining that those benchmarks are gamed and they don't reflect real-world performance well. I could tell he wasn't buying it.
When I saw Jev raise 700m, I really felt that the VC funding model is creating an unserious environment for development of AI/ML. What's rewarded is slick marketing, insider connections, and confidently expounding unrealistic visions of what your team will achieve in the next 3 months.
Under these conditions there isn't much incentive to fix benchmarks.
I remember my manager in Google Research poring through a German-English translation dataset and realizing there was a ton of invalid data inside it. When he filtered out the cruft, models showed big improvements. But the filtering was laborious and not glamorous.
At my previous company, I led the development of a retrieval embedding model called Boomerang, which focused on minimizing same-language bias. Briefly, the way most embedders are trained, they will prefer worse answers in the language that the question was asked, versus better answers in a different language. I call that same-language bias.
Anyway, an investor at a16z was quizzing me on why this particular model wasn't top-ranked on MTEB. I tried explaining that those benchmarks are gamed and they don't reflect real-world performance well. I could tell he wasn't buying it.
When I saw Jev raise 700m, I really felt that the VC funding model is creating an unserious environment for development of AI/ML. What's rewarded is slick marketing, insider connections, and confidently expounding unrealistic visions of what your team will achieve in the next 3 months.
Under these conditions there isn't much incentive to fix benchmarks.
I remember my manager in Google Research poring through a German-English translation dataset and realizing there was a ton of invalid data inside it. When he filtered out the cruft, models showed big improvements. But the filtering was laborious and not glamorous.