New Theory Suggests Chatbots Can Understand Text | Quanta Magazine
www.quantamagazine.org
Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.

I’ve been saying this for about a year since seeing the Othello GPT research, but it’s nice to see more minds changing as the research builds up.

Edit: Because people aren’t actually reading and just commenting based on the headline, a relevant part of the article:

New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.

This theoretical approach, which provides a mathematically provable argument for how and why an LLM can develop so many abilities, has convinced experts like Hinton, and others. And when Arora and his team tested some of its predictions, they found that these models behaved almost exactly as expected. From all accounts, they’ve made a strong case that the largest LLMs are not just parroting what they’ve seen before.

“[They] cannot be just mimicking what has been seen in the training data,” said Sébastien Bubeck, a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight.”

  • bionicjoey@lemmy.caEnglish
    0·
    11 months ago

    You can see clearly that it has no understanding of the wordplay. Though I’ll concede it’s impressive that it got the right answer at all.

    • kromem@lemmy.worldOPEnglish
      0·
      11 months ago

      The confabulations and in general the surface statistics stuff often gets in the way of the meat and potatoes of critical reasoning in the SotA models.

      A good example of this is trying a variation of common puzzles versus changing tokens to representations and having it repeat adjectives when working through CoT.

      Often as soon as it makes a mistake and has that mistake in context, it just has no way of correcting course. A lot of my current work is related to that and using a devil’s advocate approach to self-correction.

      But in reality, we won’t see a significant jump in things like being able to identify self-ignorance until hardware shifts in the next few years.