Thank you for adding Transformers! Many miss it which is wrong in 2022!
LLMS
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Comparison of GPT-3 and GPT-4 Parameter Counts
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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.
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Crypto Collapse Frees Hype Capacity for Language Models
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The collapse of crypto is great news for large language models, because it frees up a lot of hype capacity.
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Transformers Possess Greater Compositional Power Than MLPs
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Those tasks are irrelevant to my point. You seem to be denying that transformers have more compositional power than MLPs.
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LangChain 0.0.17: Improved Prompt Documentation
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ICYMI: LangChain version 0.0.17 Refactored and improved documentation around prompts
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Transformers Outperform MLPs for Parsing Tasks
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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.
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Transformers Represent Grammar Better Than MLPs
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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.
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Encoding Grammar in Transformers: Gradient Descent Learning Challenges
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You can encode a grammar into a transformer. The problem is gradient descent is not very good at learning it.
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Intelligence: Similarity and Compositionality in Transformer Models
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Intelligence is similarity + compositionality. Transformers' similarity is dot product of embeddings and their compositionality is attention, both still very primitive.
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LLMs Face the Same Problem as NLP: Unclear Use Cases
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The problem with LLMs is what it's always been with NLP: if people can't tell what they're good for and not, they'll wind up not using them for much.