课程: Generative AI: Working with Large Language Models
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BIG-bench
- [Instructor] Now some of the challenges with the current benchmarks were that they were two narrow in scope, including tasks like language understanding or summarization. It almost seemed like a research team would come up with some of these more basic tasks, and then a couple of months later, another research team would come up with a model that would ace these tasks. What if there were some benchmarks that had some really challenging tasks? And that's pretty much the background to BIG-bench or Beyond the Imitation Game Benchmark. A team of researchers from different institutions came up with over 200 tasks that humans perform well on but current state of the art language models don't. They also included a team of human expert writers that performed all tasks in order to provide a strong baseline, and they were allowed to use all available resources including searching the internet. The tasks are really diverse…
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内容
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GPT-34 分钟 32 秒
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GPT-3 use cases5 分钟 27 秒
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Challenges and shortcomings of GPT-34 分钟 17 秒
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GLaM3 分钟 6 秒
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Megatron-Turing NLG Model1 分钟 59 秒
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Gopher5 分钟 23 秒
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Scaling laws3 分钟 14 秒
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Chinchilla7 分钟 53 秒
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BIG-bench4 分钟 24 秒
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PaLM5 分钟 49 秒
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OPT and BLOOM2 分钟 51 秒
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GitHub models2 分钟 43 秒
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Accessing Large Language Models using an API6 分钟 25 秒
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Inference time vs. pre-training4 分钟 5 秒
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