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.”

  • @kromemOPEnglish
    0•10 months ago

    Furthermore, we presented a method for adapting a symbolic function learner to find mathematical relationships between measured quantities in an unsupervised way. This method does not explicitly label any variate as being an output value, bypassing the assumptions made in standard regression problems about causal relationships. We demonstrated how this method was able to recover rules of geometry from raw data alone. This included the law of sines and the Pythagorean theorem, two relationships on measurements relating to triangles where no variable is necessarily considered an output of a function of the others.

    • Panju, Automated Knowledge Discovery Using Neural Networks (2021)
    • @RedactedEnglish
      2•10 months ago

      Seems to me you are attempting to understand machine learning mathematics through articles.

      That quote is not a retort to anything I said.

      Look up Category Theory. It demonstrates how the laws of mathematics can be derived by forming logical categories. From that you should be able to imagine how a neural network could perform a similar task within its structure.

      It is not understanding, just encoding to arrive at correct results.

      • @kromemOPEnglish
        1•10 months ago

        What I quoted isn’t an article, it was a mathematics dissertation.

        And you disputed that a NN could arrive at the theorem before being corrected about it.

        • @RedactedEnglish
          0•10 months ago

          There you go arguing in bad faith again by putting words in my mouth and reducing the nuance of what was said.

          You do know dissertations are articles and don’t constitute any form or rigorous proof in and of themselves? Seems like you have a very rudimentary understanding of English, which might explain why you keep struggling with semantics. If that is so, I apologise because definitions are difficult when it comes to language, let alone ESL.

          I didn’t dispute that NNs can arrive at a theorem. I debate whether they truly understand the theorem they have encoded in their graphs as you claim.

          This is a philosophical/semantical debate as to what “understanding” actually is because there’s not really any evidence that they are any more than clever pattern recognition algorithms driven by mathematics.

          • @kromemOPEnglish
            0•10 months ago

            I debate whether they truly understand the theorem they have encoded in their graphs as you claim.

            Where did I claim that? Cite the exact phrase.

            I said reverse engineer. Not deduce or prove.

            • @RedactedEnglish
              1•10 months ago

              Title of your post is literally “New Theory Suggests Chatbots Can Understand Text”.

              You also hinted at it with your Pythag analogy.

              • @kromemOPEnglish
                -1•10 months ago

                Title of your post is literally “New Theory Suggests Chatbots Can Understand Text”.

                I didn’t write the headline, and I happen to interpret it the same way I interpreted it in “Bees understand the concept of zero.” Language can have more than one narrowly scoped meaning, and the article body makes it clear it isn’t saying anything about human consciousness or introspective understanding.

                You also hinted at it with your Pythag analogy.

                No, I correctly stated that a model happening upon the Pythagorean function would outperform ones approximating it by statistical correlations. That, as Hinton has said in the past, “predicting the next thing takes knowledge.” It makes sense that the development of world models and abstractions from the training data and not simply surface statistics would correlate with both increased next token prediction and network complexity increases.

                You interpreted what I was saying as implying the network has some woo woo interpretation of ‘understanding’ because you seem to be more committed to debating a straw man using inaccurate and overly narrow semantics than actually discussing the topic at hand in good faith.

                • @RedactedEnglish
                  1•10 months ago

                  You posted the article rather than the research paper and had every chance of altering the headline before you posted it but didn’t.

                  You questioned why you were downvoted so I offered an explanation.

                  Your attempts to form your own arguments often boil down to “no you”.

                  So as I’ve said all along we just differ on our definitions of the term “understanding” and have devolved into a semantic exchange. You are now using a bee analogy but for a start that is a living thing not a mathematical model, another indication that you don’t understand nuance. Secondly, again, it’s about definitions. Bees don’t understand the number zero in the middle of the number line but I’d agree they understand the concept of nothing as in “There is no food.”

                  As you can clearly see from the other comments, most people interpret the word “understanding” differently from yourself and AI proponents. So I infer you are either not a native English speaker or are trying very hard to shoehorn your oversimplified definition in to support your worldview. I’m not sure which but your reductionist way of arguing is ridiculous as others have pointed out and full of logical fallacies which you don’t seem to comprehend either.

                  Regarding what you said about Pythag, I agree and would expect it to outperform statistical analysis. That is due to the fact that it has arrived at and encoded the theorem within its graphs but I and many others do not define this as knowledge or understanding because they have other connotations to the majority of humans. It wouldn’t for instance be able to tell you what a triangle is using that model alone.

                  I spot another apeal to authority… “Hinton said so and so…” It matters not. If Hinton said the sky is green you’d believe it as you barely think for yourself when others you consider more knowledgeable have stated something which may or may not be true. Might explain why you have such an affinity for AI…