Once you've seen a few results of an LLM given too much sway over product decisions, 5 effort modes expressed as various english adjectives is pretty much par for the course
I’ve also noticed Gemini and Claude occasionally mixing terms recently (eg revel vs reveal) and can’t decide whether it is due to cost optimization effects or some attempt to seem more human.
I can’t recall either using a wrong word prior this month for some time.
Or just because mistakes are part of the distribution that it's trained on? Usually the averaging effect of LLMs and top-k selection provides some pressure against this, but occasionally some mistake like this might rise up in probability just enough to make the cutoff and get hit by chance.
I wouldn't really ascribe it to any "attempt to seem more human" when "nondeterministic machine trained on lots of dirty data" is right there.
Sure, but if that were the case why has it gotten worse recently? I would expect it to be as a result of cost optimization or tradeoffs in the model. I suppose it could be an indicator of the exhaustion of high quality training data or model architecture limitation. But this specific example, revel vs reveal, is almost like going back to GPT-2 reddit errors.
I also don’t want to pretend there is no incentive for AI to seem more human by including the occasional easily recognized error.
Or just the models are getting bigger and better at representing the long tail of the distribution. Previously errors like this would get averaged away more often; now they are capable of modelling more variation, and so are picking up on more of these kinds of errors.
There is no evidence of this. Evals are quite different from "self-evals". The only robust way of determining if LLM instructions are "good" is to run them through the intended model lots of times and see if you consistently get the result you want. Asking the model if the instructions are good shows a very deep misunderstanding of how LLMs work.
When you give prompt P to model M, when your goal is for the model to actually execute those instructions, the model will be in state S.
When you give the same prompt to the same model, when your goal is for the model to introspect on those instructions, the model is still in state S. It's the exact same input, and therefore the exact same model state as the starting point.
Introspection-mode state only diverges from execution-mode state at the point at which you subsequently give it an introspection command.
At that point, asking the model to e.g. note any ambiguities about the task at hand is exactly equivalent to asking it to evaluate any input, and there is overwhelming evidence that frontier models do this very well, and have for some time.
Asking the model, while it's in state S, to introspect and surface any points of confusion or ambiguities it's experiencing about what it's being asked to do, is an extremely valuable part of the prompt engineering toolkit.
I didn't, and don't, assert that "asking the model if the instructions are good" is a replacement for evals – that's a strawman argument you seem to be constructing on your own and misattributing to me.
At that point, asking the model to e.g. note any ambiguities about the task at hand is exactly equivalent to asking it to evaluate any input
This point is load-bearing for your position, and it is completely wrong.
Prompt P at state S leads to a new state SP'. The "common jumping off point" you describe is effectively useless, because we instantly diverge from it by using different prompts.
And even if it weren't useless for that reason, LLMs don't "query" their "state" in the way that humans reflect on their state of mind.
The idea that hallucinations are somehow less likely because you're asking meta-questions about LLM output is completely without basis
Nicely put. I haven't seen anyone say that the introspection abilities of LLMs are up to much, but claiming that it's completely impossible to get a glimpse behind the curtain is untrue.
Is that based on your "deep understanding" of how LLMs work or have you actually tried it? If you watch the execution trace of a Skill in action, you can see that it's doing exactly this inspection when the skill runs - how could it possibly work any other way?
Skills are just textual instructions, LLMs are perfectly capable of spotting inconsistencies, gaps and contradictions in them. Is that sufficient to create a good skill? No, of course not, you need to actually test them. To use an analogy, asking a LLM to critique a skill is like running lint on C code first to pick up egregious problems, running testcases is vital.
Leaving aside the sloppiness of the article, I think a lot of the behaviour in recent memory around crypto and meme stocks, and to an extent the whole rotating bubbles mode that markets seem to be in, can be attributed to this general trend.
It's harder and harder to see the traditional path from school to work to some acceptable level of family wealth as being effective/worthwhile, and so we see different flavours of roulette-with-more-steps capturing more of the population's attention.
AI is present everywhere these days. I wouldn’t be surprised if a OpenClaw bot autonomously create a project on GitHub and then submit it to HN, without any human involement.
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