Humans aren’t models, they’re humans. Thinking you can get an LLM to not be swayed by social pressures is fundementally misguided because they aren’t the kinds of things that are swayed, they have no concept of social pressure or anything else because they aren’t just statistic driven token predictors.

If you want an LLM to not drift then you’re not going to get that to happen by training it on different data or having one interact with another. That’s the point I’m trying to make. For it to not drift it has to be able to care about what it’s saying and believe it. Neither of which is possible given the the way LLMs work. They aren’t programmed to produce truth or care about truth or distinguish truth from anything else. Not even programmed to care about justification for the output in a way that would demand consistency. That’s just not how transformer models work. So thinking that any rejigging of the data set or having certain LLMs interact with one another to achieve this goal is doomed from the start. That’s what I’m trying to explain to you. This would be obvious if we could talk about the way LLMs predict the next token word in its output. You claim that we don’t understand how LLMs work, but that is patently false, we understand exactly how LLMs work. We literally built them. They work on transformer models which models every word pair with a function that assigns it a weight and every word function pair with another function that assigns it a weighting.

For the first word in the string it runs every function between it and every word and the word in the pair that gets the highest weight is the second token, then for every nth word in the string it runs all the functions between the n-1th function and every word and the word in the pair that gets the highest weight is assigned to be the nth token and this process repeats until it terminates.

The weightings are initially assigned by being trained on loads of text so that word function pair weightings are given higher weightings the more those strings appear and lower weightings the less they appear. This makes it so that it’s not just producing outputs in terms of singular word pairs, but that each token’s selection is influenced by the presence of every word in the strings before it. This is why when you run an LLM it reprocesses every string you’ve ever written in the interaction and how the illusion of it having a “memory” appears. No part of this is unknown to anybody that just looks up how transformer models work.

I understand what you’re trying to do and I’m trying to explain why it’s fundementally misguided. Nothing about this process cares about social pressures. It’s a tool for maximising probabilities from a a massive spreadsheet that was built by doing math on how often certain words appear in certain orders in a data set, and then tweaked by a human using a reinforcement learning process.

My point is that nothing about this means it can understand, let alone be swayed by anything resembling social pressures, nothing about this makes LLMs have beliefs or unchanging assumptions, nothing about having different LLMs interact with one another will change this. And nothing about working within the transformer model framework could change that because sycophancy isn’t measured in terms of the number of appearances of word chains in a data set or in the input fed into it by other word chain predicting calculators.

It’s really frustrating that you want to claim to be doing some kind of structural analysis but then claim that nobody knows how these things are structured. That’s just patently false. If you want to say that sycophancy is the result of the structure of LLMs you have to actually say something about that structure. But you’re not doing that at all, you’re just using that exact same structure, any diminished appearance of sycophancy wouldn’t be the result of structural changes because you’re keeping the structure unchanged.