[vladsh]

LLMs get over-analyzed. They’re predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psychology” in any human sense.

Agents, however, are products. They should have clear UX boundaries: show what context they’re using, communicate uncertainty, validate outputs where possible, and expose performance so users can understand when and why they fail.

IMO the real issue is that raw, general-purpose models were released directly to consumers. That normalized under-specified consumer products, created the expectation that users would interpret model behavior, define their own success criteria, and manually handle edge cases, sometimes with severe real world consequences.

I’m sure the market will fix itself with time, but I hope more people would know when not to use these half baked AGI “products”

[DuperPower]

because they wanted to sell the illusion of consciousness, chatgpt, gemini and claude are humans simulator which is lame, I want autocomplete prediction not this personality and retention stuff which only makes the agents dumber.

[metalliqaz]

Since their goal is to acquire funding, it is much less important for the product to be useful than it is for the product to be sci-fi.

Remember when the point was revenue and profits? Man, those were the good old days.

[nowittyusername]

You hit the nail on the head. Anyone who's been working intimately with LLM's comes to the same conclusion. the llm itself is only one small important part that is to be used in a more complicated and capable system. And that system will not have the same limitations as the raw llm itself.

[andreyk]

To say they LLMs are 'predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psychology” in any human sense.' is not entirely accurate. Classic LLMs like GPT 3 , sure. But LLM-powered chatbots (ChatGPT, Claude - which is what this article is really about) go through much more than just predict-next-token training (RLHF, presumably now reasoning training, who knows what else).

[mrbungie]

> go through much more than just predict-next-token training (RLHF, presumably now reasoning training, who knows what else).

Yep, but...

> To say they LLMs are 'predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psychology” in any human sense.' is not entirely accurate.

That's a logical leap, and you'd need to bridge the gap between "more than next-token prediction" to similarity to wetware brains and "systems with psychology".

[more_corn]

Sure, but they reflect all known human psychology because they’ve been trained on our writing. Look up the anthropic tests. If you make an agent based on an LLM it will display very human behaviors including aggressive attempts to prevent being shut down.

[basch]

they are human in the sense they are reenforced to exhibit human like behavior, by humans. a human byproduct.

[NebulaStorm456]

Is the solution to sycophancy just a very good clever prompt that forces logical reasoning? Do we want our LLMs to be scientifically accurate or truthful or be creative and exploratory in nature? Fuzzy systems like LLMs will always have these kinds of tradeoffs and there should be a better UI and accessible "traits" (devil's advocate, therapist, expert doctor, finance advisor) that one can invoke.

[adleyjulian]

> LLMs get over-analyzed. They’re predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psychology” in any human sense.

Per the predictive processing theory of mind, human brains are similarly predictive machines. "Psychology" is an emergent property.

I think it's overly dismissive to point to the fundamentals being simple, i.e. that it's a token prediction algorithm, when it's clear to everyone that it's the unexpected emergent properties of LLMs that everyone is interested in.

[xoac]

The fact that a theory exists does not mean that it is not garbage

[imiric]

The difference is that we know how LLMs work. We know exactly what they process, how they process it, and for what purpose. Our inability to explain and predict their behavior is due to the mind-boggling amount of data and processing complexity that no human can comprehend.

In contrast, we know very little about human brains. We know how they work at a fundamental level, and we have vague understanding of brain regions and their functions, but we have little knowledge of how the complex behavior we observe actually works. The complexity is also orders of magnitude greater than what we can model with current technology, but it's very much an open question whether our current deep learning architectures are even the right approach to model this complexity.

So, sure, emergent behavior is neat and interesting, but just because we can't intuitively understand a system, doesn't mean that we're on the right track to model human intelligence. After all, we find the patterns of the Game of Life interesting, yet the rules for such a system are very simple. LLMs are similar, only far more complex. We find the patterns they generate interesting, and potentially very useful, but anthropomorphizing this technology, or thinking that we have invented "intelligence", is wishful thinking and hubris. Especially since we struggle with defining that word to begin with.

[kcexn]

A large part of that training is done by asking people if responses 'look right'.

It turns out that people are more likely to think a model is good when it kisses their ass than if it has a terrible personality. This is arguably a design flaw of the human brain.