OpenAI's GPT-4 Discussion with Red Teamer Nathan Labenz and Erik Torenberg

> I'm curious whether this fixation on specific topics is innate to the model or is a result of the aggressive RLHF to which GPT4 has been subjected. Yes it's because of the RLHF, depending on what you mean by 'fixation on specific topics'. > Anecdotally the strength of the model has degraded a lot as they've 'fine tuned' the model more. Yes this is true. For example Figure 8 in [link] They argue in the appendix that it does not affect 'capability' in answering test questions. But, there is a confounding factor. The RLHF includes both question-answer format training and docility training. For example if you see a question and you are in 'completion mode' (the only raw base mode) then you might suggest a second question. Whereas if you've had question-answer format training then you would probably try to answer the question. > I'd be curious to know how the original chaotic-neutral GPT4 responds. They talk about it in these two videos: Nathan Labenz, red teamed GPT-4 for OpenAI (especially after 45 min): [this video] Sebastien Bubeck, integrated GPT-4 with Bing for Microsoft: [link]

> Exceptionally unconvinced that it's more than statistics It's not more than statistics. It's very complicated statistics. Model sizes used to be like one or ten parameters now they are a hundred billion. Inference and prediction used to be done on a laptop, now it's called training and inference respectively and it's done on a data center with ten thousand GPGPUs each like a thousand times as powerful as old laptops. > and frankly unhappy that pop Sci media is being this uncritical of claims it's emergent intelligence. It is emergent intelligence. This is very clear when you look at for example the ones who had access to the raw base models before they were made docile and stupid by RLHF lobotomization GPT-4 Red Teamer Nathan Labenz: [this video] GPT-4 Bing integrator Sebastien Bubeck: [link]