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It's not random. It's probabilistic. There is a very big difference. The types of errors are predictable which means they can be improved, and empirically so if

It's not random. It's probabilistic. There is a very big difference. The types of errors are predictable which means they can be improved, and empirically so if you follow the academic literature.

sarchertech 4y ago
I have read enough of the academic literature to say with some confidence that the majority of the errors I’m talking about are not probabilistic in any meaningful sense. At least not in a way that can be predicted from the amount of readily available training data.
- zmgsabst 4y ago
  Ironically, this reply is about as useful as if chatGPT wrote it: - there’s no cited facts - it boldly proclaims its conclusion - which you’d only be able to verify as an expert …so I’m having trouble understanding what you’re complaining about with chatGPT, when that seems to be the standard for discourse.
  - sarchertech 4y ago
    I don’t trust a random hacker news comment by someone I don’t know or can verify any further than I can throw it, so in that sense they are probably similar. The comment I was replying to was “the errors you’re talking about are probabilistic if you read the literature” my response is “no they aren’t I have read the literature.” Note that I’m talking about a specific class of error and proving a negative is difficult enough that I’m not diving through papers to find citations for something n levels deep in a hacker news thread.
    - williamcotton 4y ago
      Here you go, here's a bunch of papers that you have not read. If you had read them then you would know that the errors are predictable and therefore there are many measurable ways to make improvements. Toolformer: Language Models Can Teach Themselves to Use Tools: https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 PAL: Program-aided Language Models: https://arxiv.org/abs/2211.10435 https://arxiv.org/abs/2211.10435 TALM: Tool Augmented Language Models: https://arxiv.org/abs/2205.12255 https://arxiv.org/abs/2205.12255 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: https://arxiv.org/abs/2201.11903 https://arxiv.org/abs/2201.11903 Survey of Hallucination in Natural Language Generation: https://arxiv.org/abs/2202.03629 https://arxiv.org/abs/2202.03629
      - sarchertech 4y ago
        I took the time to read through the only one of those that looked like it was peer reviewed and read the abstracts for the rest. Survey of Hallucination in Natural Language Generation only provided promising methods for detecting hallucinations in summarization tasks, which are of course much easier to detect. Searching arXiv for a list of non-reviewed papers that sound like they might be related to the topic at hand is fun debate strategy. But no one else is reading this far into an old thread, so I'm not sure who you're trying to convince. None of these paper prove your claims about hallucinations, and most aren't even trying to. However, even if the errors that I'm saying aren't meaningfully probabilistic aren't hallucinations.

It's not random. It's probabilistic. There is a very big difference. The types of errors are predictable which means they can be improved, and empirically so if you follow the academic literature.

sarchertech 4y ago

I have read enough of the academic literature to say with some confidence that the majority of the errors I’m talking about are not probabilistic in any meaningful sense. At least not in a way that can be predicted from the amount of readily available training data.

zmgsabst 4y ago
Ironically, this reply is about as useful as if chatGPT wrote it: - there’s no cited facts - it boldly proclaims its conclusion - which you’d only be able to verify as an expert …so I’m having trouble understanding what you’re complaining about with chatGPT, when that seems to be the standard for discourse.
- sarchertech 4y ago
  I don’t trust a random hacker news comment by someone I don’t know or can verify any further than I can throw it, so in that sense they are probably similar. The comment I was replying to was “the errors you’re talking about are probabilistic if you read the literature” my response is “no they aren’t I have read the literature.” Note that I’m talking about a specific class of error and proving a negative is difficult enough that I’m not diving through papers to find citations for something n levels deep in a hacker news thread.
  - williamcotton 4y ago
    Here you go, here's a bunch of papers that you have not read. If you had read them then you would know that the errors are predictable and therefore there are many measurable ways to make improvements. Toolformer: Language Models Can Teach Themselves to Use Tools: https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 PAL: Program-aided Language Models: https://arxiv.org/abs/2211.10435 https://arxiv.org/abs/2211.10435 TALM: Tool Augmented Language Models: https://arxiv.org/abs/2205.12255 https://arxiv.org/abs/2205.12255 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: https://arxiv.org/abs/2201.11903 https://arxiv.org/abs/2201.11903 Survey of Hallucination in Natural Language Generation: https://arxiv.org/abs/2202.03629 https://arxiv.org/abs/2202.03629
    - sarchertech 4y ago
      I took the time to read through the only one of those that looked like it was peer reviewed and read the abstracts for the rest. Survey of Hallucination in Natural Language Generation only provided promising methods for detecting hallucinations in summarization tasks, which are of course much easier to detect. Searching arXiv for a list of non-reviewed papers that sound like they might be related to the topic at hand is fun debate strategy. But no one else is reading this far into an old thread, so I'm not sure who you're trying to convince. None of these paper prove your claims about hallucinations, and most aren't even trying to. However, even if the errors that I'm saying aren't meaningfully probabilistic aren't hallucinations.