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Comment by kazinator | Hacker News Reader

It's all very clear when you mentally replace "LLM" with "text completion driven by compressed training data".

E.g.

[Text copletion driven by compressed training data] exhibit[s] a puzzling inconsistency: [it] solves complex problems yet frequently fail[s] on seemingly simpler ones.

Some problems are better represented by a locus of texts in the training data, allowing more plausible talk to be generated. When the problem is not well represented, it does not help that the problem is simple.

If you train it on nothing but Scientology documents, and then ask about the Buddhist perspective on a situation, you will probably get some nonsense about body thetans, even if the situation is simple.

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I have a hard time trying to conceptualize lossy text compression, but I've recently started to think about the "reasoning"/output as just a by product of lossy compression, and weights tending towards an average of the information "around" the main topic of prompt. What I've found easier is thinking about it like lossy image compression, generating more output tokens via "reasoning" is like subdividing nearby pixels and filling in the gaps with values that they've seen there before. Taking the analogy a bit too far, you can also think of the vocabulary as the pixel bit depth.

I definitely agree replacing AI or LLMs with "X driven by compressed training data" starts to make a lot more sense, and a useful shortcut.

You're right about "reasoning". It's just trying to steer the conversation in a more relevant direction in vector space, hopefully to generate more relevant output tokens. I find it easier to conceptualize this in three dimensions. 3blue1brown has a good video series which covers the overall concept of LLM vectors in machine learning: https://youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_...

To give a concrete example, say we're generating the next token from the word "queen". Is this the monarch, the bee, the playing card, the drag entertainer? By adding more relevant tokens (honey, worker, hive, beeswax) we steer the token generation to the place in the "word cloud" where our next token is more likely to exist.

I don't see LLMs as "lossy compression" of text. To me that implies retrieval, and Transformers are a prediction device, not a retrieval device. If one needs retrieval then use a database.

> You're right about "reasoning". It's just trying to steer the conversation in a more relevant direction in vector space, hopefully to generate more relevant output tokens.

I like to frame it as a theater-script cycling through the LLM. The "reasoning" difference is just changing the style so that each character has film noir monologues. The underlying process hasn't really changes, and the monologues text isn't fundamentally different from dialogue or stage-direction... but more data still means more guidance for each improv-cycle.

> say we're generating the next token from the word "queen". Is this the monarch, the bee, the playing card, the drag entertainer?

I'd like to point out that this scheme can result in things that look better to humans in the end... even when the "clarifying" choice is entirely arbitrary and irrational.

In other words, we should be alert to the difference between "explaining what you were thinking" versus "picking a firm direction so future improv makes nicer rationalizations."

It makes sense if you think of the LLM as building a data-aware model that compresses the noisy data by parsimony (the principle that the simplest explanation that fits is best). Typical text compression algorithms are not data-aware and not robust to noise.

In lossy compression the compression itself is the goal. In prediction, compression is the road that leads to parsimonious models.

It is not a useful shortcut because you don't know what the training data is, nothing requires it to be an "average" of anything, and post-training arbitrarily re-weights all of its existing distributions anyway.

The way I visualize it is imagining clipping the high frequency details of concepts and facts. These things operate on a different plane of abstraction than simple strings of characters or tokens. They operate on ideas and concepts. To compress, you take out all the deep details and leave only the broad strokes.

One day people will say "we used to think the devil is in the details, but now we know it is in their removal".

This is true, but also misleading. We are learning that the models achieve compression by distilling higher level concepts and deriving generalized human like abilities, for example the recent introspection paper from Anthropic.

Well, that's what a LLM is. The problem is if one's mental model is built on "AI" instead of "LLM."

The fact that LLMs can abstract concepts and do any amount of out-of-sample reasoning is impressive and interesting, but the null hypothesis for a LLM being "impressive" in any regard is that the data required to answer the question is present in it's training set.

oh man i am pretty tired of the “it’s just autocomplete” armchair warriors… it is an accurate metaphor in only the most pedantic of ways, and has zero explanatory power whatsoever as far as intuition building goes. and i don’t even understand the impulse. “reality is easy, it’s just quantum autocomplete!”

Why shouldn't you expect a problem's simplicity to correlate tremendously with how well it is represented in training data? Every angle I can think of tilts in that direction. Simpler problems are easier to remember and thus repeat, they come up more often, asd they require less space/time/effort to record (which also means they are less likely to contain errors).

> Text copletion driven by compressed training data...solves complex problems

Sure it does. Obviously. All we ever needed was some text completion.

Thanks for your valuable insight.

Thank you for posting this. I'm struck with how there is a lot of studying of the behavior and isolating it from other assumptions and then these individual capabilities are then described as a new solution or discovered capability that would work with all of those other assumptions. This makes most all of the LLM research feel like whack a mole if the goal was to make accurate and reliable models by understanding these techniques. Instead, it's more like seeing faces in cars and buildings and other artifacts of patterns and pattern groupings and recognition of patterns. Building houses on sand, etc.

> It's all very clear when you mentally replace "LLM" with "text completion driven by compressed training data".

This isn't what LLMs are, of course, but what some political groups insist they are so they can strengthen copyright law by pointing to LLMs as "theft". It's all very pro-Disney, of course.

This is a popular take on HN yet incomplete in its assessment of LLMs and their capabilities.

> It's all very clear when you mentally replace "LLM" with "text completion driven by compressed training data".

So you replace a more useful term with a less useful one?

Is that due to political reasons?

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