LLMS

→ View original post on X — @reach_vb

Whisper Paper Reading: OpenAI’s Speech Recognition Model

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@reach_vb

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21 November 2022 18h19

Don't forget to read the paper ahead of the reading: https://
openai.com/blog/whisper/

→ View original post on X — @deeplearn007

Transformers Finally Added: Critical AI Architecture Recognition

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@deeplearn007

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21 November 2022 16h59

Thank you for adding Transformers! Many miss it which is wrong in 2022!

→ View original post on X — @aibreakfast

Comparison of GPT-3 and GPT-4 Parameter Counts

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@aibreakfast

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21 November 2022 16h53

Currently, GPT-3 has 175 billion parameters, which is 10x faster than any of its closest competitors. GPT-4 is rumored be about 100 trillion parameters.

Crypto Collapse Frees Hype Capacity for Language Models

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21 November 2022 7h28

The collapse of crypto is great news for large language models, because it frees up a lot of hype capacity.

Transformers Possess Greater Compositional Power Than MLPs

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21 November 2022 7h07

Those tasks are irrelevant to my point. You seem to be denying that transformers have more compositional power than MLPs.

→ View original post on X — @langchain

LangChain 0.0.17: Improved Prompt Documentation

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@langchain

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21 November 2022 4h48

ICYMI: LangChain version 0.0.17 Refactored and improved documentation around prompts

Transformers Outperform MLPs for Parsing Tasks

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21 November 2022 4h08

Transformers are better than MLPs far from the limit. And these papers are about something else. All it takes for my statement to be correct is that a transformer can learn to parse correctly in some cases.

Transformers Represent Grammar Better Than MLPs

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21 November 2022 3h50

No, transformers can represent grammar better than (say) MLPs, which is an advance. And they can learn it to a very limited degree, so what I said is accurate.