What Emily Bender meant by "stochastic parrots"

The contention that there is no grounding because the training data is linguistic and thus can only reference a world model is disproven in "This sentence has five words"- there's real, grounded information about what "five" means within that sentence. While that's a trivial counterexample, I don't know that it's an obvious one (I didn't come up with it myself).

It's not a criticism of the paper itself, but multimodal models came shortly after and provide grounding that is more of the sort the paper is getting at, and it didn't seem like anybody updated on that at all. If multimodal models were still stochastic parrots by the original argument, humans would have to be as well; we don't have any way to ground anything beneath sense data and evolution can't have programmed some innate grounding into us because it didn't either. But (and maybe this is my own misperception) nobody threw in the towel at that point.

I confess I never read the original paper until now, opting to absorb by osmosis instead, and I was quite surprised that they don't really make a deeper case than that. After just a few paragraphs about how they can't be grounded because humans don't express their thoughts directly, it lurches into a page about how they can be biased by training. And they certainly can be, but that has little to say about their stochastic nature- humans are biased as a rule with no exception. (For the record, I only read the Stochastic Parrots section before this reply.)

It's not really a bad paper, but I don't see why it ever carried the esteem it did. Hating on it is like hating on Taylor Swift- she's fine, yes, but for her level of success, one is inclined to question every dumb lyric where others get a pass. (Apologies to Swift fans, substitute a successful artist you don't care for here.)

Even if pre-training was the only training step, it still wouldn't necessarily follow that the only thing the model is doing is stitching words together probabilistically, unless you expand the definition of "probabilistically" to the point that it becomes meaningless. This kind of thinking assumes that design of the training process and the "design" of the artifact that training produces must be similar.

Statistical operation doesn’t preclude logical processing.

We’ve know that since 1943 when McCulloch-Pitts came up with the first “artificial neuron” definition. And since LLMs are a descendant technology — our assumption should be they’re reasoning in some internal learned logic.

This is what the evidence supports — eg, the “stochastic parrot” crowd never can explain transfer learning. Whereas for the internal reasoning crowd that is easy: removing your top level judgments from a theory still leaves you with useful terms for describing a new theory — eg, removing your judgments about “which animal is this?” but preserving the underlying structure for representing an image in your new judgments, “is this cancer?”

There’s 80 years of reason to think DNNs reason and zero support other than “sTaTs R mAgIc!” to support the stochastic parrot interpretation.

Ignorance isn’t argument.

The authors were wrong about their core thesis and are now lying about it. That's the only criticism needed. They said, quote:

> LMs are not performing natural language understanding (NLU), and only have success in tasks that can be approached by manipulating linguistic form

... which is presented as unarguable fact, yet is untrue. It was obviously wrong at the time it was written and it's been proven wrong in many ways since. Worse is that they're still at it. In the article she's saying:

> Q: What are the most common misconceptions about the “stochastic parrots” metaphor? Bender: I think one of the biggest ones is, “Bender says AI is a stochastic parrot.”

Her name is on a paper titled "On the danger of stochastic parrots". It has a section titled "Stochastic parrots" and in section 6.1 it says:

> An LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.

She did say that, as clear as day. Now she's trying to rewrite history. Ugly behavior.

let's compare ai water usage with something even more useless, water leaking out of pipes. the United States currently loses about 2 trillion gallons of water annually to leaking pipes. this is in comparison to around 230 billion gallons used by data centers.

we're literally dumping several times the amount of water used by data centers onto the ground for no benefit at all. oddly enough I haven't seen any protests about this despite how concerned everyone is about water usage.