I found the aeticle in a post on the fediverse, and I can’t find it anymore.

The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

Then they asked the LLM to explain how it found the result, what was it’s internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

This showed 2 things:

  • LLM don’t “know” how they work

  • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

I think it was a very interesting an meaningful analysis

Can anyone help me find this?

EDIT: thanks to @theunknownmuncher @lemmy.world https://www.anthropic.com/research/tracing-thoughts-language-model its this one

EDIT2: I’m aware LLM dont “know” anything and don’t reason, and it’s exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

  • theunknownmuncher@lemmy.worldEnglish
    6·
    3 days ago

    I don’t want to brigade, so I’ll put my thoughts here. The linked comment is making the same mistake about self preservation that people make when they ask an LLM to “show it’s work” or explain it’s reasoning. The text response of an LLM cannot be taken at it’s word or used to confirm that kind of theory. It requires tracing the logic under the hood.

    Just like how it’s not actually an AI assistant, but trained and prompted to output text that is expected to be what an AI assistant would respond with, if it is expected that it would pursue self preservation, then it will output text that matches that. It’s output is always “fake”

    That doesn’t mean there isn’t a real potential element of self preservation, though, but you’d need to dig and trace through the network to show it, not use the text output.