alt Hacker NewsThis essay could probably benefit from some engagement with the literature on “interpretability” in LLMs, including the empirical results about how knowledge (like addition) is represented inside the neural network. To be blunt, I’m not sure being smart and reasoning from first principles after asking the LLM a lot of questions and cherry picking what it gets wrong gets to any novel insights at this point. And it already feels a little out date, with LLMs getting gold on the mathematical Olympiad they clearly have a pretty good world model of mathematics. I don’t think cherry-picking a failure to prove 2 + 2 = 4 in the particular specific way the writer wanted to see disproves that at all.
LLMs have imperfect world models, sure. (So do humans.) That’s because they are trained to be generalists and because their internal representations of things are massively compressed single they don’t have enough weights to encode everything. I don’t think this means there are some natural limits to what they can do.
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With LLMs being unable to count how many Bs are in blueberry, they clearly don't have any world model whatsoever. That addition (something which only takes a few gates in digital logic) happens to be overfit into a few nodes on multi-billion node networks is hardly a surprise to anyone except the most religious of AI believers.
I think both the literature on interpretability and explorations on internal representations actually reinforce the author's conclusion. I think internal representation research tends to nets that deal with a single "model" don't necessary have the same representation and don't necessarily have a single representation.
And doing well on XYZ isn't evidence of a world model in particular. The point that these things aren't always using a world is reinforced by systems being easily confused by extraneous information, even systems as sophisticated as thus that can solve Math Olympiad questions. The literature has said "ad-hoc predictors" for a long time and I don't think much has changed - except things do better on benchmarks.
And, humans too can act without a consistent world model.
Any suggestions from this literature?
Your being blunt is actually very kind, if you're describing what I'm doing as "being smart and reasoning from first principles"; and I agree that I am not saying something very novel, at most it's slightly contrarian given the current sentiment.
My goal is not to cherry-pick failures for its own sake as much as to try to explain why I get pretty bad output from LLMs much of the time, which I do. They are also very useful to me at times.
Let's see how my predictions hold up; I have made enough to look very wrong if they don't.
Regarding "failure disproving success": it can't, but it can disprove a theory of how this success is achieved. And, I have much better examples than the 2+2=4, which I am citing as something that sorta works these says