In computer chess there's a concept of "contempt". When you set the engine to have high contempt it evaluates the opponent's moves lower, essentially assuming that the opponent will make a mistake. Conversely with low contempt the engine evaluates the opponent's moves higher, expecting the opponent to play better than it.

There is a similar trade-off with LLMs. Sometimes their human conversant wants assistance and so the LLM should be more deferential. At other times the human wants a bias towards correctness rather than their own opinions.

It would be nice to have a contempt knob that you can adjust, instead of blindly trying to emulate one through prompting.

Love when Opus 4.6 tells me that I am actually wrong about some detail and that it's right (which after validating it often turns out to be true) - breath of fresh air.

Not having friction to saying "Prove it, write a shell script or code test to demonstrate." is also great, though sometimes doing that proves both of our assumptions to be wrong.

The article posits that sycophancy is inherent to how models are trained.

I think there's a simpler explanation. Every leaked system prompt from every model pretty much includes instructions to "be helpful," and the models are trained to be assistants, not just general knowledge repositories or research tools.

My hunch is that's the core of the problem -- the system prompt.

The article's main idea is that for an AI, sycophancy or adversarial are the two available modes because they don't have enough context to make defensible decisions. You need to include a bunch of fuzzy stuff around the situation, far more than it strictly "needs" to help it stick to its guns and actually make decisions confidently

I think this is interesting as an idea. I do find that when I give really detailed context about my team, other teams, ours and their okrs, goals, things I know people like or are passionate about, it gives better answers and is more confident. but its also often wrong, or overindexes on these things I have written. In practise, its very difficult to get enough of this on paper without a: holding a frankly worrying level of sensitive information (is it a good idea to write down what I really think of various people's weaknesses and strengths?) and b: spending hours each day merely establishing ongoing context of what I heard at lunch or who's off sick today or whatever, plus I know that research shows longer context can degrade performance, so in theory you want to somehow cut it down to only that which truly matters for the task at hand and and and... goodness gracious its all very time consuming and im not sure its worth the squeeze

An AI can only be tuned to either be sycophantic or adversarial.

It isn't possible to tune an AI to have some sort of 'correct answer' orientation because that would be full AGI.

Something a little annoying is when doing a google search the AI section can change day-by-day and even between devices.

I was thinking through a coding architecture issue and did a google search a few days ago on my phone and one of the ideas it gave me was really useful so I left the browser open. Of course when I went to look at it again today the page reloaded and the results weren't the same or as good. So I tried the same search on my desktop and it was even worse.

Well of course. If I trawl the world's collected texts, "Are you sure?" correlates very strongly with the following words being an indication that previous statements were erroneous.

If I used that body of text to make a statistical model and used that model to predict what comes after "Are you sure?" it would very often be an indication that previous statements were erroneous.

This doesn't feel like a problem but a feature. I don't want AI making ambiguous decisions. I want it to research, present all the relevant facts, and seek my approval before choosing a direction. I even append my prompts like "present all the risks and benefits of each option" rather than "make a decision" to avoid getting a confident answer.

I feel like if I asked the author of this entire article "are you sure?", they might change their mind...

This is real, but (at least in a coding context) easily preventable. Just append "don't assume you're wrong - investigate" or something to that effect. Annoying, but usually effective.

I am seriously tired of every other paragraph I read ending in an It isn't just X, it's Y. I'm sure there is something insightful in between this slop but to the author: Please write using your own voice, if I wanted ChatGPT's take on it I would ask.

I call this self-repudiation. I performed a systematic experiment on this exact matter, a couple of years ago. I found that ChatGPT 3.5 frequently self-repudiated, whereas 4.0, under identical circumstances, rarely did.

These experiments are a bit expensive to run because you are forced to read all the responses to judge repudiation. Sometimes it is subtle.

Also, behavior changes with the exact wording of the question.

I'm more curious about the other direction. How many times has a model replied to a request with "Are you sure?" I'd bet just about zero.

In my job I do it all the time; people ask for stuff and I often spend a lot of time on clarifying questions, the most fundamental of which is "Are you sure this is what you want?"

Except when I wanted to get ChatGPT or Claude to criticize a religion or religious figure, namely Khamenei. It never backed down and if forced too much and I pointed out its contradiction, it would switch to 2~3-word sentences response mode (i.e. passive-aggressive).

It was a long time ago, Claude 3 or maybe ChatGPT's v3. It felt so dehumanizing that I never tried again.

It didn't seem like trained behavior though, it felt much like hardcoded behavior.

AI Slop. Unfortunately

AI slop about AI slop. The internet is dead.

There isn’t a mind to change. Unfortunately the article is slop. Too bad, won’t read the rest.

I wish there was a tag or something we could put on headlines to avoid giving views to slop.