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In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data, and the very predictive hypo

In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data, and the very predictive hypothesis one would draw from that is the one I put in my original hypothesis, and that was insightfully expanded upon by the person that replied to me. That model produces a hypothesis that fully accords with the experiment and does so with fewer assumptions about things like theory of mind, consciosness, or agency, which are all associated with "deception" Now, the model with the least assumptions is not always the most productive model, but here it works exceedingly well. The paper would have been stronger (less contestible) if framed in that model, but would admittedly have received less attention.

famouswaffles 3y ago
>In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data There was no document in GPT-4's training that looked anything like what the transcript of this experiment looked like so that's pretty wrong. It behaves like how people who wrote its training set might have behaved is more accurate not only because of the better model but literally because the predictor's goal is to reverse engineer the casual processes that led to that output. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 A Language Model fed on protein sequences has biological structure and function emerge in the inner layers because those are the processes that could have led to that output. If you say, it generated text that looks like its training then why does it not stop at alphanumeric sequences that simply look like protein sequences? Why is it generating actual novel proteins ? https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Because looking like the training data is not actually the goal. Modelling it is.

famouswaffles 3y ago

>In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data There was no document in GPT-4's training that looked anything like what the transcript of this experiment looked like so that's pretty wrong. It behaves like how people who wrote its training set might have behaved is more accurate not only because of the better model but literally because the predictor's goal is to reverse engineer the casual processes that led to that output. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 A Language Model fed on protein sequences has biological structure and function emerge in the inner layers because those are the processes that could have led to that output. If you say, it generated text that looks like its training then why does it not stop at alphanumeric sequences that simply look like protein sequences? Why is it generating actual novel proteins ? https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Because looking like the training data is not actually the goal. Modelling it is.