Melanie Mitchell Sparks Debate: Today's Systems Are Not LLMs

Starting September 27, Santa Fe Institute professor Melanie Mitchell argued on X that "what we have now are not LLMs," igniting a sprawling debate over the Stochastic Parrots paper and the definition of "LLM." Her core claim: a language model proper captures the statistical structure of language (n-grams, Elman networks, GPT-2), whereas today's systems undergo heavy post-training and embed many external software components, so attacking them with "stochastic parrots" is a straw man—the paper targeted 2021-era LLMs. As of publication, the dispute remains an exchange of views with no resolution.

Confirmed

• Mitchell clashed with Boaz Barak (University of Chicago) over definitions: Barak asked whether "LLM" and "stochastic parrot" apply only to pre-InstructGPT models, noting people have called the newer systems LLMs all along; Mitchell reiterated her view of the language-model lineage and said she only wants to correct the term's misuse—today it denotes any large AI system containing a pretrained language model, not its original meaning in the paper.
• mmitchellai clarified the paper's scope: "LLM" there meant "pretrained," "unsupervised" language models, reflecting the authors' mental model at writing time; they also noted having co-written with Emily Bender a piece explaining how "stochastic parrot" refers to LLMs.
• Google DeepMind's Andrew Lampinen posted a long thread countering Bender & Koller's core technical claim—that systems trained only on language learn mere form and no grounding-dependent meaning—arguing meaning can be learned from language alone and calling "stochastic parrot" itself "much ado about nothing," ironically mirroring the authors' own misunderstanding of language.
• Redwood Research's Blanche Minerva listed a five-step pipeline (pretrain on text, teach tool-calling tokens, teach the command line, etc.) to refute "LLMs are just next-token predictors," and asked Mitchell whether a transformer with command-line access that runs programs but does no input/output processing counts as a stochastic parrot—and whether the criterion depends on training method.
• Further technical pushback: francip argued pretraining, SFT, preference optimization and RL all modify the same weights and the same inference process yielding a token conditional distribution, so post-training alone cannot demarcate "language model"; DeepMind's Nathaniel Daw added that instruction tuning and RLHF shifting behavior away from pure next-token prediction has been true since ChatGPT. lusichu rebutted that Mitchell misread the paper's meaning.
• The paper's authors responded: Timnit Gebru said she would no longer engage with people obsessed with the argument, asking "why does an accurate description of LLMs upset you so much"; cloneofsimo quote-posted with an image.
• Others backed Mitchell: Brad Buchsbaum said the paper "ornithomorphizes" LLMs; tallinzen—rarely on the paper's side—argued word meanings shift over time, and judging the 2021 paper by today's systems is anachronistic; Oxford's Michael Bronstein said the classic critique, even with generous discounts such as stripping away surrounding software, should be retired. @aidanclark separately mocked imagegen failures as evidence current models are "dumb as a brick," far from ASI.

Unconfirmed

• No consensus exists on the boundary of "LLM": whether post-RLHF, tool-using systems still count as language models, and whether the "stochastic parrot" critique is thereby invalidated, remain unresolved points of contention.

Why it matters

• The dispute touches on whether one of AI's most-cited critical frameworks still applies: if today's systems differ in composition and capability from 2021's LLMs, safety and ethics discussions built on "stochastic parrots" may need to recalibrate their terminology and targets.

2026-09-27 ~ 2026-09-28 · 31 related posts

Full story(2 episodes)→

• 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)

• Researcher pushes back on 'stochastic parrot' framing: even pure LLMs no longer fit it — BlancheMinerva · 2026-09-27
• Mitchell points to his and Emily Bender's essays on how "stochastic parrot" refers to LLMs — mmitchell_ai · 2026-09-27
• Melanie Mitchell: 'Stochastic parrots' is a strawman against RL-post-trained models — MelMitchell1 · 2026-09-27
• Blanche Minerva's five-step pipeline shows why "just next-token prediction" no longer fits LLMs — BlancheMinerva · 2026-09-27
• Researcher asks: a transformer with a command line — still a 'stochastic parrot'? — BlancheMinerva · 2026-09-27
• Dev challenges "stochastic parrot" critics: a transformer with a shell — how many lines until it's one? — BlancheMinerva · 2026-09-27
• A meme answers Timnit Gebru's 'stochastic parrots' defiance — cloneofsimo · 2026-09-27
• Mitchell: stochastic parrot critique targeted 2021 LLMs, not today's AI systems — PMinervini · 2026-09-27
• Mel Mitchell argues today's AI systems are no longer LLMs, term misuse muddies debate — _aidan_clark_ · 2026-09-27
• User roasts imagegen being 'dumb as bricks' as evidence we're nowhere near ASI — _aidan_clark_ · 2026-09-27
• Melanie Mitchell backs claim that today's systems aren't really LLMs, citing original definition — MelMitchell1 · 2026-09-27
• Melanie Mitchell vs DeepMind Co-founder: Do Today's Models Still Count as 'Stochastic Parrots'? — _aidan_clark_ · 2026-09-27
• Boaz Barak pushes back on Mitchell: were 'stochastic parrots' only pre-2022 models? — boazbaraktcs · 2026-09-27
• Melanie Mitchell vs. Boaz Barak: does 'stochastic parrots' still apply to today's LLMs? — MelMitchell1 · 2026-09-27
• Melanie Mitchell: language models span n-grams to GPT-2, agentic systems are another matter — MelMitchell1 · 2026-09-27
• [source] Melanie Mitchell argues RLHF-trained models are no longer true language models — MelMitchell1 · 2026-09-27
• Melanie Mitchell: Post-Training Like RLHF Makes Models No Longer Statistical Language Models — MelMitchell1 · 2026-09-27
• Debate: raw LLMs without any harness already refute the 'stochastic parrot' claim — Zergylord · 2026-09-27
• Ex-Google Research VP Aidan Clark: "What We Have Now Are Not LLMs" — giffmana · 2026-09-27
• [source] Google researcher Lampinen pens long thread rebutting the stochastic parrots argument on LLM meaning — AndrewLampinen · 2026-09-27
• Researcher sides with the stochastic parrots paper: criticizing it with today's LLMs is anachronistic — PMinervini · 2026-09-27
• 'Stochastic parrot' debate reignites: researchers clash over the 2021 critique's relevance — FlorianGallwitz · 2026-09-27
• Critic lands a sharp line: the stochastic parrot paper's real flaw is that it ornithomorphizes LLMs — _onionesque · 2026-09-27
• Stochastic Parrots co-author hits back: paper was about 2021 LLMs, not today's AI systems — ctjlewis · 2026-09-27
• DeepMind's Nathaniel Daw: the post-training critique has held since ChatGPT — burny_tech · 2026-09-27
• Researchers debate: does post-training stop a transformer from being a language model? — burny_tech · 2026-09-27
• Melanie Mitchell calls Stochastic Parrot critiques strawmen; lu_sichu pushes back — burny_tech · 2026-09-27
• Oxford's Bronstein: time to put the "stochastic parrots" argument to rest — mmbronstein · 2026-09-28
• [source] Mitchell clarifies paper's LLM definition, sparking debate on post-trained models — mmitchell_ai · 2026-09-28
• Margaret Mitchell clarifies disputed paper was about pretrained base models — mmitchell_ai · 2026-09-28

1 near-duplicate retellings: asusarla