4 ms·

Indeed. And telling it to spell out the steps in math solution is essentially (ab)using the chat log as the memory for an iteration loop. This demonstrates the

Indeed. And telling it to spell out the steps in math solution is essentially (ab)using the chat log as the memory for an iteration loop. This demonstrates then when you instruct it "break down the problem and solve it iteratively" it comes out with superior solutions. More accurate, more novel. Ergo, then if it was allowed to iterate in this way internally, it'd produce better answers by default. This thread sums up the same idea: https://twitter.com/ItIsFinch/status/1634730897520951296 https://twitter.com/ItIsFinch/status/1634730897520951296

ad404b8a372f2b9 4y ago
Yeah, I think it's going to be a promising area of research. It's known as Chain-Of-Thought (CoT) in the literature and there are a few papers already out: https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=qFVVBzXxR2V https://openreview.net/forum?id=qFVVBzXxR2V I'm also looking forward to explicit long-term memory mechanisms that don't rely on the main architecture's weights nor on CoT.
fock 4y ago
There's no memory or iteration log. You are just priming the (more or less deterministic even...) predictor with some history. You are doing a lot of work yourself, setting a factually correct context (and this has severe limits...). As for "we just need a way to include iterations": isn't this what attention is supposed to do (kind of dynamically updating weights). The usual way to really update weights are variations on gradient descent. Can you link a paper outlining how we integrate your proposal into the current framework (which took around 20years to mature)? Otherwise your statements are Sci-Fi (at the flying cars-level).
- BulgarianIdiot 4y ago
  Oh so there's no memory, just recallable history. Oh so it's not learning by example, it's just primed by a sample. Oh so there's no iteration of thought, just a loop of prediction. OK. Also, the papers you asked about: https://news.ycombinator.com/item?id=35115563 https://news.ycombinator.com/item?id=35115563 BTW, people are already implementing recursive/iterative queries on the ChatGPT API, and getting promising results.
  - LawTalkingGuy 4y ago
    The langchain project is an example of the iterative queries approach. It comes with constructs for working memory, factual lookup / calculation agents, etc. https://github.com/hwchase17/langchain https://github.com/hwchase17/langchain The general (non-technical) guideline is that the LLMs can "answer" anything you just gave them the answer for. So you give it a problem, ask it how to solve it, tell it to use that method and explain the data it needs, give it that data, and then show it everything at once: "With this data you requested and summarized, use this technique to answer this question".

ad404b8a372f2b9 4y ago

Yeah, I think it's going to be a promising area of research. It's known as Chain-Of-Thought (CoT) in the literature and there are a few papers already out: https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=qFVVBzXxR2V https://openreview.net/forum?id=qFVVBzXxR2V I'm also looking forward to explicit long-term memory mechanisms that don't rely on the main architecture's weights nor on CoT.

fock 4y ago

There's no memory or iteration log. You are just priming the (more or less deterministic even...) predictor with some history. You are doing a lot of work yourself, setting a factually correct context (and this has severe limits...). As for "we just need a way to include iterations": isn't this what attention is supposed to do (kind of dynamically updating weights). The usual way to really update weights are variations on gradient descent. Can you link a paper outlining how we integrate your proposal into the current framework (which took around 20years to mature)? Otherwise your statements are Sci-Fi (at the flying cars-level).

BulgarianIdiot 4y ago
Oh so there's no memory, just recallable history. Oh so it's not learning by example, it's just primed by a sample. Oh so there's no iteration of thought, just a loop of prediction. OK. Also, the papers you asked about: https://news.ycombinator.com/item?id=35115563 https://news.ycombinator.com/item?id=35115563 BTW, people are already implementing recursive/iterative queries on the ChatGPT API, and getting promising results.
- LawTalkingGuy 4y ago
  The langchain project is an example of the iterative queries approach. It comes with constructs for working memory, factual lookup / calculation agents, etc. https://github.com/hwchase17/langchain https://github.com/hwchase17/langchain The general (non-technical) guideline is that the LLMs can "answer" anything you just gave them the answer for. So you give it a problem, ask it how to solve it, tell it to use that method and explain the data it needs, give it that data, and then show it everything at once: "With this data you requested and summarized, use this technique to answer this question".