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> >LLMs do not use logic at all. The logic of invalidation is missing. > This is a fascinating idea that i see that just doesn't square with reality. In fact,

> >LLMs do not use logic at all. The logic of invalidation is missing. > This is a fascinating idea that i see that just doesn't square with reality. In fact, this is all they do. What do you imagine training to be ? I could have been more clear, but I didn't want to write a novel. The ambiguity here is what they invalidate: memory reconstructions, not logical assertions. An LLM can't tell the difference between fact and fiction, because it can't apply logic. Better memory will never suddenly spawn itself the feature to think objectively about that memory. LLMs improve, yes, but they didn't start as a poor-quality equivalent to human thought. They started out as a poor quality equivalent to human memory. > There is a projection of the world in text. Text is the world and the language model is very much interacting with it. The language model becomes that world. It does not inhabit it. It does not explore. It does not think, it only knows.

famouswaffles 3y ago
>An LLM can't tell the difference between fact and fiction, because it can't apply logic. Not true. They can differentiate it just fine. Of course being able to tell the difference and being incentivized to communicate it are 2 different things. GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r https://imgur.com/a/3gYel9r Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975 https://arxiv.org/abs/2305.14975 Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334 https://arxiv.org/abs/2205.14334 Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221 https://arxiv.org/abs/2207.05221 The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets - https://arxiv.org/abs/2310.06824 https://arxiv.org/abs/2310.06824
- thomastjeffery 3y ago
  > Not true. They can differentiate it just fine. I'm not convinced. I think the reality has been lost in its presentation. It's always a human who makes the assertion of truthiness. The LLM does not make determinations or assertions: it only remembers them. When we "train" an LLM, we structure it along our intentions. It never restructures itself. It never acts.

famouswaffles 3y ago

>An LLM can't tell the difference between fact and fiction, because it can't apply logic. Not true. They can differentiate it just fine. Of course being able to tell the difference and being incentivized to communicate it are 2 different things. GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r https://imgur.com/a/3gYel9r Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975 https://arxiv.org/abs/2305.14975 Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334 https://arxiv.org/abs/2205.14334 Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221 https://arxiv.org/abs/2207.05221 The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets - https://arxiv.org/abs/2310.06824 https://arxiv.org/abs/2310.06824

thomastjeffery 3y ago
> Not true. They can differentiate it just fine. I'm not convinced. I think the reality has been lost in its presentation. It's always a human who makes the assertion of truthiness. The LLM does not make determinations or assertions: it only remembers them. When we "train" an LLM, we structure it along our intentions. It never restructures itself. It never acts.