LLMs do all the things people say they do at once, even if those people vociferously disagree, and they’re all useful.

Some interesting quotes:

  1. LLMs do both of the things that their promoters and detractors say they do.
  2. They do both of these at the same time on the same prompt.
  3. It is very difficult from the outside to tell which they are doing.
  4. Both of them are useful.

When a search engine is able to do this, it is able to compensate for a limited index size with intelligence. By making reasonable inferences about what page text is likely to satisfy what query text, it can satisfy more intents with fewer documents.

LLMs are not like this. The reasoning that they do is inscrutable and massive. They do not explain their reasoning in a way that we can trust is actually their reasoning, and not simply a textual description of what such reasoning might hypothetically be.

@AutoTLDR

  • AutoTLDR@programming.devBEnglish
    2·
    1 year ago

    TL;DR: (AI-generated 🤖)

    The text discusses the debate surrounding LLMs (large language models) and their abilities. Detractors view them as blurry and nonsensical, while promoters argue that they possess sparks of AGI (artificial general intelligence) and can learn complex concepts like multivariable calculus. The author believes that LLMs can do both of these things simultaneously, making it difficult to distinguish which task they are performing. They introduce the concepts of “memorization” and “generalization” to describe the different aspects of LLMs’ capabilities. They argue that a larger index size, similar to memorization, allows search engines to satisfy more specific queries, while better language understanding and inference, similar to generalization, allows search engines to go beyond the text on the page. The author suggests using the terms “integration” and “coverage” instead of memorization and generalization, respectively, to describe LLMs. They explain that LLMs’ reasoning is inscrutable and that it is challenging to determine the level of abstraction at which they operate. They propose that the properties of search engine quality, such as integration and coverage, are better analogies to understand LLMs’ capabilities.

    NOTE: This summary may not be accurate. The text was longer than my maximum input length, so I had to truncate it.

    Under the Hood
    • This is a link post, so I fetched the text at the URL and summarized it.
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