In this episode of Machine Learning Street Talk, Tim Scarfe, Yannic Kilcher and Connor Shorten discuss their takeaways from OpenAI’s GPT-3 language model. With the help of Microsoft’s ZeRO-2 / DeepSpeed optimiser, OpenAI trained an 175 BILLION parameter autoregressive language model. The paper demonstrates how self-supervised language modelling at this scale can perform many downstream tasks without fine-tuning.

00:00:00 Intro

00:00:54 ZeRO1+2 (model + Data parallelism) (Connor)

00:03:17 Recent history of NLP (Tim)

00:06:04 Yannic "Light-speed" Kilcher's brief overview of GPT-3

00:14:25 Reviewing Yannic's YT comments on his GPT-3 video (Tim)

00:20:26 Main show intro

00:23:03 Is GPT-3 reasoning?

00:28:15 Architecture discussion and autoregressive (GPT*) vs denoising autoencoder (BERT)

00:36:18 Utility of GPT-3 in industry

00:43:03 Can GPT-3 do math? (reasoning/system 1/system 2)

00:51:03 Generalisation

00:56:48 Esoterics of language models

00:58:46 Architectural trade-offs

01:07:37 Memorization machines and intepretability

01:17:16 Nearest neighbour probes / watermarks

01:20:03 YouTube comments on GPT-3 video

01:21:50 GPT-3 news article generation issue

01:27:36 Sampling data for language models / bias / fairness / politics

01:51:12 Outro

These paradigms of task adaptation are divided into zero, one, and few shot learning. Zero-shot learning is a very extreme case where we expect a language model to perform a task such as sentiment classification or extractive question answering, without any additional supervision. One and Few-shot learning provide some examples to the model. However, GPT-3s definition of this diverges a bit from the conventional literature. GPT-3 provides one and few-shot examples in the form of “In-Context Learning”. Instead of fine-tuning the model on a few examples, the model has to use the input to infer the downstream task. For example, the GPT-3 transformer has an input sequence of 2048 tokens, so demonstrations of a task such as yelp sentiment reviews, would have to fit in this input sequence as well as the new review.

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Paper Links:

GPT-3: https://arxiv.org/abs/2005.14165

ZeRO: https://arxiv.org/abs/1910.02054

ZeRO (Blog Post): https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/

ZeRO-2 (Blog Post): https://www.microsoft.com/en-us/research/blog/zero-2-deepspeed-shattering-barriers-of-deep-learning-speed-scale/?OCID=msr_blog_deepspeed2_build_tw

#machinelearning #naturallanguageprocessing #deeplearning #gpt3

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