Researchers Debate Whether "LLMs Are Just Next-Token Predictors" Is a Meaningless Claim
On Sep 25–26, researchers clashed on Twitter/Bluesky over the popular claim that "LLMs are just next-token predictors / stochastic parrots." Aaroth opened by arguing the phrase is contentless: any mapping from input to output strings can be decomposed into a series of next-token predictions, so the claim conveys nothing substantive about LLMs. The debate then extended to whether human brains are isomorphic to LLMs.
Confirmed
- Aaroth posted on Sep 25, adding historical context: ten years ago, whether representing output distributions as next-token distributions posed computational difficulties was a genuinely interesting question, but practice showed the worry never materialized.
- Princeton's Aran Nayebi relayed the debate on Sep 26, noting participants including MIT's Andrew Ilyas and Aaron Roth; Aaron Roth similarly argued the "next-token prediction" framing lacks content.
- A second thread pitted Aaroth against @10001plateaus over brain mechanisms. @10001plateaus countered that saying "the brain picks the next word by producing ML scores" is flatly wrong: brains can think non-sequentially, and they have a mechanism for choosing "what to say" unlike an LLM's.
- Aaroth responded that any probability distribution can be written as a product of element-wise conditional distributions (exactly the LLM output form), yet this does not mean the producing system is inherently sequential; one cannot infer cognitive limits from a representational choice.
Why it matters
- The debate highlights a common misjudgment of LLM capability boundaries: conflating output form (token-by-token generation) with internal mechanism or reasoning capacity (inability to think non-sequentially), an error made by both LLM detractors and hypesters.
- It also links two threads: the decade-old learnability question about next-token representation has been settled by practice, while whether the human brain is a "prediction machine" remains open.
2026-09-25 ~ 2026-09-26 · 7 related posts
- Episode 1: Researchers Debate Whether "LLMs Are Just Next-Token Predictors" Is a Meaningless Claim(2026-09-25, 7 posts)
- Episode 2: Melanie Mitchell Sparks Debate: Today's Systems Are Not LLMs(2026-09-27, 31 posts)
Primary sources
- 'Next-token predictor' is a contentless way to describe LLMs — Aaroth · 2026-09-25
- [source] The decade-old worry that next-token representation would hinder learning turned out unfounded — Aaroth · 2026-09-25
- [source] Why 'LLMs are just next-token predictors' is a contentless argument — Aaroth · 2026-09-25
- [source] Researchers call 'next token predictor' a contentless critique of LLMs — aran_nayebi · 2026-09-26
- "Brains aren't next-token predictors": the counterpoint in an LLM-vs-brain debate — 10001plateaus · 2026-09-26
- LLM output distributions being factored doesn't mean they can't think out of order — Aaroth · 2026-09-26
1 near-duplicate retellings: Aaroth