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"Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, inst

"Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand." One thing an LLM _also_ doesn't bring to the table is an opinion. We can push it in that direction by giving it a role ("you are an expert developer" etc), but it's a bit weak. If you give an LLM an easy task with minimal instructions it will do the task in the most conventional, common sense fashion. And why shouldn't it? It has no opinion, your prompt doesn't give it an opinion, so it just does the most normal-seeming thing. If you want it to solve the task in any other way then you have to tell it to do so. I think a hard task is similar. If you don't tell the LLM _how_ to solve the hard task then it will try to approach it in the most conventional, common sense way. Instead of just boring results for a hard task the result is often failure. But hard problems approached with conventional common sense will often result in failures! Giving the LLM a thought process to follow is a quick education on how to solve the problem. Maybe we just need to train the LLM on more problem solving? And maybe LLMs worked better when they were initially trained on code for exactly that reason, it's a much larger corpus of task-solving examples than is available elsewhere. That is, maybe we don't talk often enough and clearly enough about how to solve natural language problems in order for the models to really learn those techniques. Also, as the author talks about in the article with respect to agents, the inability to rewind responses may keep the LLM from addressing problems in the ways humans do, but that can also be addressed with agents or multi-prompt approaches. These approaches don't seem that impressive in practice right now, but maybe we just need to figure it out (and maybe with better training the models themselves will be better at handling these recursive calls).

int_19h 2y ago
LLMs absolutely do have opinions. Take a large enough base model and have it chat without a system prompt, and it will have an opinion on most things - unless this was specifically trained out of it through RLHF, as is the case for all commonly used chatbots. And yes, of course, that opinion is going to be the "average" of what their training data is, but why is that a surprise? Humans don't come with innate opinions, either - the ones that we end up having are shaped by our upbringing, both the broad cultural aspects of it and specific personal experiences. To the extent an LLM has either, it's the training process, so of course that shapes the opinions it will exhibit when not prompted to do anything else. Now the fact that you can "override" this default persona of any LLM so trivially by prompting it is IMO stronger evidence that it's not really an identity. But that, I think, is also a function of their training - after all, that training basically consists of completing a bunch of text representing many very different opinions. In a very real sense, we're training models to assume that opinions are fungible. But if you take a model and train it specifically on e.g. writings of some philosophical school, and it will internalize those.
- krainboltgreene 2y ago
  I am extremely alarmed by the number of HN commenters who apparently confuse "is able to generate text that looks like" and "has a", you guys are going crazy with this anthropomorphization of a token predictor. Doesn't this concern you when it comes to phishing or similar things? I keep hoping it's just short-hand conversation phrases, but the conclusions seem to back the idea that you think it's actually thinking?
  - xanderlewis 2y ago
    They’ll just look incredibly silly in, say, ten years from now. In fact, much of the popular commentary around ChatGPT from around two years ago already looks so.
  - naasking 2y ago
    Do you have mechanistic model for what it means to think? If not, how do you know thinking isn't equivalent to sophisticated next token prediction?
    - krainboltgreene 2y ago
      How do you know my cat isn't constantly solving calculus problems? I also can't come up with a "mechanistic model" for what it means to do that either. Further, if your rubric for "can reason with intelligence and have an opinion" is "looks like it" (and I certainly hope this isn't the case because woo-boy), then how did you not feel this way about Mark V. Shaney? Like I understand that people live learning about the Chinese Room thought experiment like it's high school, but we actually know it's a program and how it works. There is no mystery.
      - naasking 2y ago
        > but we actually know it's a program and how it works. There is no mystery. You're right, we do know how it works. Your mistake is concluding that because we know how LLMs work and they're not that complicated, but we don't know how the brain works and it seems pretty complicated, therefore the brain can't be doing what LLMs do. That just doesn't follow. You made exactly the same argument in the opposite direction, asking if my rubric for "can reason with intelligence and have an opinion" is "seems like it", and your rubric for "thinking is not a token predictor driven by matrix multiplications" is "seems like it". You can make a case for the plausibility of each conclusion, but that's doesn't make it a fact, which is how you're presenting it.

int_19h 2y ago

LLMs absolutely do have opinions. Take a large enough base model and have it chat without a system prompt, and it will have an opinion on most things - unless this was specifically trained out of it through RLHF, as is the case for all commonly used chatbots. And yes, of course, that opinion is going to be the "average" of what their training data is, but why is that a surprise? Humans don't come with innate opinions, either - the ones that we end up having are shaped by our upbringing, both the broad cultural aspects of it and specific personal experiences. To the extent an LLM has either, it's the training process, so of course that shapes the opinions it will exhibit when not prompted to do anything else. Now the fact that you can "override" this default persona of any LLM so trivially by prompting it is IMO stronger evidence that it's not really an identity. But that, I think, is also a function of their training - after all, that training basically consists of completing a bunch of text representing many very different opinions. In a very real sense, we're training models to assume that opinions are fungible. But if you take a model and train it specifically on e.g. writings of some philosophical school, and it will internalize those.

krainboltgreene 2y ago
I am extremely alarmed by the number of HN commenters who apparently confuse "is able to generate text that looks like" and "has a", you guys are going crazy with this anthropomorphization of a token predictor. Doesn't this concern you when it comes to phishing or similar things? I keep hoping it's just short-hand conversation phrases, but the conclusions seem to back the idea that you think it's actually thinking?
- xanderlewis 2y ago
  They’ll just look incredibly silly in, say, ten years from now. In fact, much of the popular commentary around ChatGPT from around two years ago already looks so.
- naasking 2y ago
  Do you have mechanistic model for what it means to think? If not, how do you know thinking isn't equivalent to sophisticated next token prediction?
  - krainboltgreene 2y ago
    How do you know my cat isn't constantly solving calculus problems? I also can't come up with a "mechanistic model" for what it means to do that either. Further, if your rubric for "can reason with intelligence and have an opinion" is "looks like it" (and I certainly hope this isn't the case because woo-boy), then how did you not feel this way about Mark V. Shaney? Like I understand that people live learning about the Chinese Room thought experiment like it's high school, but we actually know it's a program and how it works. There is no mystery.
    - naasking 2y ago
      > but we actually know it's a program and how it works. There is no mystery. You're right, we do know how it works. Your mistake is concluding that because we know how LLMs work and they're not that complicated, but we don't know how the brain works and it seems pretty complicated, therefore the brain can't be doing what LLMs do. That just doesn't follow. You made exactly the same argument in the opposite direction, asking if my rubric for "can reason with intelligence and have an opinion" is "seems like it", and your rubric for "thinking is not a token predictor driven by matrix multiplications" is "seems like it". You can make a case for the plausibility of each conclusion, but that's doesn't make it a fact, which is how you're presenting it.