[maxilevi]

LLMs are just really good search. Ask it to create something and it's searching within the pretrained weights. Ask it to find something and it's semantically searching within your codebase. Ask it to modify something and it will do both. Once you understand its just search, you can get really good results.

[fennecbutt]

I agree somewhat, but more when it comes to its use of logic - it only gleans logic from human language which as we know is a fucking mess.

I've commented before on my belief that the majority of human activity is derivative. If you ask someone to think of a new kind of animal, alien or random object they will always base it off things that they have seen before. Truly original thoughts and things in this world are an absolute rarity and the majority of supposed original thought riffs on what we see others make, and those people look to nature and the natural world for inspiration.

We're very good at taking thing a and thing b and slapping them together and announcing we've made something new. Someone please reply with a wholly original concept. I had the same issue recently when trying to build a magic based physics system for a game I was thinking of prototyping.

[andy99]

  it only gleans logic from human language

This isn’t really true, at least how I interpret the statement, little if any of the “logic” or appearance of such is learned from language. It’s trained in with reinforcement learning as pattern recognition.

Point being it’s deliberate training, not just some emergent property of language modeling. Not sure if the above post meant this, but it does seem a common misconception.

[onemoresoop]

LLMs lack agency in the sense that they have no goals, preferences, or commitments. Humans do, even when our ideas are derivative. We can decide that this is the right choice and move forward, subjectively and imperfectly. That capacity to commit under uncertainty is part of what agency actually is.

[bhadass]

better mental model: it's a lossy compression of human knowledge that can decompress and recombine in novel (sometimes useful, sometimes sloppy) ways.

classical search simply retrieves, llms can synthesize as well.

[TeMPOraL]

Corporate wants you to find the difference...

Point being, in broad enough scope, search and compression and learning are the same thing. Learning can be phrased as efficient compression of input knowledge. Compression can be phrased as search through space of possible representation structures. And search through space of possible X for x such that F(x) is minimized, is a way to represent any optimization problem.

[RhythmFox]

This isn't strictly better to me. It captures some intuitions about how a neural network ends up encoding its inputs over time in a 'lossy' way (doesn't store previous input states in an explicit form). Maybe saying 'probabilistic compression/decompression' makes it a bit more accurate? I do not really think it connects to your 'synthesize' claim at the very end to call it compression/decompression, but I am curious if you had a specific reason to use the term.

[nextaccountic]

Maybe the base model is just a compression of the training data?

There is also a RLHF training step on top of that

[andy99]

No, this describes the common understanding of LLMs and adds little to just calling it AI. The search is the more accurate model when considering their actual capabilities and understanding weaknesses. “Lossy compression of human knowledge” is marketing.

[DebtDeflation]

Information Retrieval followed by Summarization is how I view it.

[andrei_says_]

“Novel” to the person who has not consumed the training data. Otherwise, just training data combined in highly probable ways.

Not quite autocomplete but not intelligence either.

[dcre]

I really don’t think search captures the thing’s ability to understand complex relationships. Finding real bugs in 2000 line PRs isn’t search.

[cultureulterior]

This is not true.

[andoando]

Im not sure how anyone can say this. It is really good search, but its also able to combine ideas and reason about and do fairly complex logic on tasks surely absolutely no one has asked before.

[__MatrixMan__]

Its a very useful model but not a complete one. You just gotta acknowledge that if you're making something new its gonna take all day and require a lot of guard rails, but then you can search for that concept later (add the repo to the workspace and prompt at it) and the agent will apply it elsewhere as if it was a pattern in widespread use. "Just search" doesn't quite fit. I've never wondered how best to use a search engine to make something in a way that will be easily searchable later.

[johnisgood]

Calling it "just search" is like calling a compiler "just string manipulation". Not false, but aggressively missing the point.

[emp17344]

No, “just search” is correct. Boosters desperately want it to be something more, but it really is just a tool.

[maxilevi]

I don't mean search in the reductionist way but rather that its much better at translating, finding and mapping concepts if everything is provided vs creating from scratch. If it could truly think it would be able to bootstrap creations from basic principles like we do, but it really can't. Doesn't mean its not a great powerful tool.

[godelski]

  > Once you understand its just search, you can get really good results.

I think this is understating the issue, ignoring context. It reminds me of how easy people claim searching is with search engines. But there's so many variables that can make results change dramatically. Just like Google search, two people can type in the exact same query and get very different results. But probably the bigger difference is in what people are searching for.

What's problematic with these types of claims is that they just come off as calling anyone who thinks differently dumb. It's as disconnected as saying "It's intuitive" in one breath and "You're holding it wrong" in another. It's a bad mindset to be in as an engineer because someone presents a problem and instead of trying to address it is dismissed. If someone is holding it wrong, it probably isn't intuitive[0]. Even if they can't explain the problem correctly, they are telling you a problem exists[1]. That's like 80% of the job of an engineer: figuring out what the actual problem is.

As maybe an illustrative example people joke that a lot of programming is "copy pasting from stack overflow". We all know the memes. There's definitely times where I've found this to be a close approximation to writing an acceptable program. But there's many other times where I've found that to be far from possible. There's definitely a strong correlation to what type of programming I'm doing, as in what kind of program I'm writing. Honestly, I find this categorical distinction not being discussed enough with things like LLMs. Yet, we should expect there to be a major difference. Frankly, there are just different amounts of information on different topics. Just like how LLMs seem to be better with more common languages like Python than less common languages (and also worse at just more complicated languages like C or Rust).

[0] You cannot make something that's intuitive to all people. But you can make it intuitive for most people. We're going to ignore the former case because the size should be very small. If 10% of your users are "holding it wrong" then the answer is not "10% of your users are absolute morons" it is "your product is not as intuitive as you think." If 0.1% of your users are "holding it wrong" then well... they might be absolute morons.

[1] I think I'm not alone in being frustrated with the LLM discourse as it often feels like people trying to gaslight me into believing the problems I experience do not exist. Why is it so surprising that people have vastly differing experiences? *How can we even go about solving problems if we're unwilling to acknowledge their existence?*