@yanmar_lonking755: #CapCut

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Saturday 20 June 2026 05:37:50 GMT
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OpenAI Research Lead Tejal Patwardhan tells the story of how they realized their benchmarks weren't measuring what actually matters:
OpenAI Research Lead Tejal Patwardhan tells the story of how they realized their benchmarks weren't measuring what actually matters: "We were really having this crisis of evals where we kept training successively better models. And on Swe-Bench, they looked about the same because they were just doing really well. And we were like, man, we have no idea how to measure what people actually want to use our models for. And so there was very much a hey, like the Bureau of Labor Statistics has a list of all the top jobs and all the top tasks for a job. And if you're a financial analyst, like doing an investment diligence or writing a legal memo or writing a paper based on a piece of research... And the idea was, can we actually ask the model those tasks that someone would want in real life with the context they would have at the time and then see how the model could solve those tasks? And at the time, when we tested one of the earliest models on this benchmark, it got like less than 20 percent. Like if you compare how well a model would do on this well-specified work task compared to a human, the model was way worse. I think now there's a lot more focus on how can we make these models useful for people in their real work, like for real scientists. And this kind of helps catalyze a wake up call that, hey, maybe we should also think about how to measure how stuff is used in the real world." — Tejal Patwardhan on the @OpenAI podcast with Andrew Mayne #tejalpatwardhan #andrewmayne #openai #aibenchmarks #airesearch

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