The Persistence of 'Stochastic Parrots': LLMs and the False Promise of Universality

When Bender, Gebru, McMillan-Major, and Shmitchell published their critique of large language models in 2021, it was received as a timely intervention. Four years on, it reads more like a structural autopsy. Their article, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, challenged not only the technical arc of natural language processing but its epistemic foundations. At its core stood a disarmingly simple claim: fluency is not understanding, and scale is not neutrality.

Generative models now mediate writing, summarize litigation, score exams, and draft legislation. Yet the concerns the authors raised—semantic incoherence, data opacity, performative alignment, and the absence of agency—remain structurally unresolved. This essay returns to their argument not as retrospective validation but as an indictment of inaction: the field listened, quoted, and moved on.

Cited Widely, Implemented Narrowly

To say that Bender et al.’s warnings have been “largely unheeded” is rhetorically forceful but requires qualification. Their article has been cited extensively in AI ethics, digital humanities, and STS. Terms like stochastic parrots, value-sensitive design, and documentation debt now anchor critical conversations.¹

Yet this discursive uptake has not translated into institutional transformation. In academia, publication metrics still favor technical performance over ethical reflection. In industry, synthetic content is scaled without commensurate investment in dataset governance. In regulation, policies such as the EU AI Act and U.S. Executive Orders focus more on managing outputs than reshaping development conditions (European Commission, 2024; OSTP, 2022).

The article’s recommendations—risk-aware design, archival documentation, and participatory foresight—have yet to be mainstreamed into research or regulatory practice. The structural critique remains valid because the structure itself remains largely intact.

Simulated Coherence and the Persistence of Misrecognition

Bender et al. called attention to a basic but easily ignored feature of large language models: they do not know what they are saying. They predict text based on statistical proximity, not semantic intention. And yet their outputs often appear coherent—fluent, topical, even persuasive. That illusion has facilitated uncritical adoption across high-trust domains.

In 2025, models like GPT-4 and Claude 2.1 populate classrooms, chat interfaces, search engines, and word processors. They are fast, responsive, and stylistically precise. What they are not is grounded. Yet their users—many of them trained professionals—frequently treat their responses as if they were authored by reasoning agents. That interpretive mistake is now well documented (Gilardi et al., 2023; Chi et al., 2023). Bender et al. anticipated this confusion. What they could not fully predict was how quickly it would scale into infrastructure.

The deeper problem, however, is not the illusion of coherence itself. It is the trust it generates—quietly, cumulatively—without friction. Once embedded into routine decision-making, synthetic language becomes self-justifying. It is read as meaningful because it resembles meaning.

Amplified Bias and Uninterrupted Data Inequity

The authors argued that LLMs trained on scraped web corpora reproduce dominant cultural logics—particularly those associated with Anglophonic hegemony. That claim has been widely corroborated. What remains under-acknowledged is the persistence of those logics even in so-called “aligned” models. Reinforcement learning has improved tone; it has not rewritten the archive.

Most current systems still draw from datasets assembled without consent, documentation, or cultural context (Birhane et al., 2023). These corpora are not neutral aggregations of linguistic behavior. They are material records of unequal participation in digital publics. And yet the same pipeline persists: scrape, pretrain, deploy. Some developers have introduced filters or blocklists, but few interrogate the structural conditions that shape what enters the model in the first place.

This is where Bender et al.’s notion of “documentation debt” remains especially potent. It names not only the absence of metadata or provenance, but the ethical deferral that has come to define dataset assembly. Transparency is promised later. Consent is assumed, not secured. Archival responsibility is replaced by scale.

What these systems also lack—and what most post-hoc alignment regimes fail to address—is cultural intelligence. Large language models possess no capacity to interpret meaning across cultural frames. They cannot distinguish between positionalities, negotiate contested norms, or recognize when language encodes historical trauma or structural exclusion. Cultural intelligence (CQ in the vernacular) requires more than linguistic fluency; it demands contextual discernment, sensitivity to difference, and the ability to situate utterances within plural interpretive traditions. In its absence, models generate what appears to be neutral output but is, in fact, culturally encoded in ways that remain unacknowledged. The result is not only bias but epistemic violence: the erasure of alternative linguistic worlds beneath the performance of universality.

The Risk of Fluency-Driven Trust

In their 2021 analysis, Bender et al. warned that linguistic fluency would become a proxy for credibility. That warning has materialized—not only in casual misreadings but also in institutional practice. Today, LLMs are embedded in environments where trust is assumed by default: courtroom workflows, medical consultations, and university help desks. Few users ask how these systems produce language, and fewer still are told.

The danger here is not merely individual misjudgment. It is the infrastructural normalization of synthetic speech as truth-adjacent. Chi et al. (2023) document how even technically literate users conflate fluency with factual reliability, particularly when outputs mimic the discursive cues of professionalism. This is not accidental; it is designed.

Most LLM interfaces offer no provenance, citations, or visible markers of synthetic authorship. Where disclaimers exist, they are peripheral or perfunctory. What matters is that the model sounds right. And when it does, it is rarely questioned. Bender et al. understood this mechanism before it became common knowledge: fluency does not prove comprehension, but it often displaces the need for it.

What makes this shift epistemically corrosive is not the presence of errors—it is the absence of epistemic friction. There is no interpretive pause, no moment of doubt, just plausible text and silent assent.

Unresolved Agency and the Problem of Attribution

Language has consequences. It makes claims, assigns responsibility, and invites reply. But when generated by LLMs, those consequences hang in the air, unattached to speaker, writer, or agent. This is the gap Bender et al. diagnosed with precision: models generate text that mimics speech acts but offer no locus of accountability.

The problem is no longer theoretical. Synthetic content now circulates under the banners of governments, corporations, and institutions. In many cases, there is no disclosure. In others, responsibility is fragmented—outsourced to end users, buried in platform disclaimers, or dismissed as downstream misuse.

What complicates matters further is that LLM outputs often function rhetorically like authored text. They persuade, reassure, or issue commands. But there is no author in the ethical sense. No one who stands behind the statement. No one who can answer for it, revise it, or be held to it.

Developers frequently frame these systems as tools. Yet the outputs are engineered to simulate dialogic exchange—to respond in ways that resemble human reasoning. This simulation is persuasive precisely because it is incomplete. It performs the social cues of authorship without assuming the burdens that come with it.

And so the problem persists. Language is produced, consumed, acted upon—without responsibility, without reply, without repair.

Scale Without Reform

Model size has grown by orders of magnitude since 2021. The underlying institutional logic has not. Academic prestige still rewards benchmark performance; corporate valuations still hinge on product deployment speed. In this environment, scale is not merely tolerated—it is celebrated.

Bender et al. warned against mistaking magnitude for progress. Their critique of “scaling as default” remains unsolved. What has changed is the sophistication of alignment techniques—RLHF, constitutional AI, and post-hoc moderation. These measures treat risk as behavioral deviation rather than systemic consequence. They optimize outputs. They do not interrogate why the model exists in the first place.

What is missing is a rethinking of objectives. What problem does a trillion-parameter model solve that a curated, participatory system cannot? Why do efficiency, equity, and harm mitigation still operate as secondary considerations?

Crucially, no dominant alignment paradigm incorporates cultural intelligence as a design imperative. LLMs are engineered to mimic language, not to interpret its plural meanings. The assumption that surface coherence can substitute for intercultural reasoning reflects a deeper epistemic failure: the belief that neutrality can be synthetically produced without reference to social context or historical specificity.

The technical ambition has advanced. The ethical framework remains stalled.

From Timely Intervention to Continuing Imperative

On the Dangers of Stochastic Parrots remains, in 2025, a diagnostic document of remarkable foresight and unfulfilled relevance. Its core insight—that language models, absent communicative intent, simulate speech without responsibility—has only grown more urgent as generative systems embed themselves into everyday informational infrastructures.

What the authors offered was not merely a critique of model architecture but a framework for ethically reorienting the entire field. Their emphasis on the social function of language, the necessity of dataset stewardship, and the limits of scale-first thinking continues to define the frontier of critical AI scholarship. The fact that these prescriptions remain under-implemented is not an indictment of their clarity but of the institutional inertia that resists change.

To revisit Bender et al. in 2025 is not only to assess their predictive accuracy but to confront the ethical and epistemic responsibilities that remain unfulfilled. Their warnings endure because the conditions that provoked them—opacity, asymmetry, harm without accountability—have not been structurally resolved.

Among these unfulfilled responsibilities is the failure to recognize cultural intelligence as foundational rather than supplemental. Language models now generate text that circulates globally, but are incapable of mediating across cultures. Without CQ, the illusion of universality becomes a mechanism of erasure. Any future development that aspires to ethical relevance must move beyond alignment toward intercultural accountability, where meaning is not only modeled but situated, and difference is not reduced to deviation.

References

Birhane, A., Prabhu, V. and Kahembwe, E., 2023. The values encoded in machine learning datasets. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23). [online] ACM. Available at: https://doi.org/10.1145/3593013.3594055.

Bender, E.M., Gebru, T., McMillan-Major, A. and Shmitchell, S., 2021. On the dangers of stochastic parrots: Can language models be too big?. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). New York: ACM, pp.610–623. Available at: https://doi.org/10.1145/3442188.3445922.

Chi, E.H., et al., 2023. Who gets it right? Behavior and performance of LLMs and humans on common misconceptions. arXiv preprint. Available at: https://arxiv.org/abs/2305.08851.

European Commission, 2024. EU Artificial Intelligence Act: Provisional Agreement on the First Worldwide Rules for AI. [online] Council of the European Union. Available at: https://www.consilium.europa.eu/en/press/press-releases/2023/12/09/artificial-intelligence-act-council-and-parliament-strike-a-deal-on-the-first-worldwide-rules-for-ai/.

Gilardi, F., Gessler, T. and Kubli, M., 2023. ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30), p.e2305016120. Available at: https://doi.org/10.1073/pnas.2305016120.

Lucy, L. and Bamman, D., 2021. Gender and representation bias in GPT-3 generated stories. In: Proceedings of the 3rd Workshop on Narrative Understanding. Association for Computational Linguistics, pp.48–55. Available at: https://aclanthology.org/2021.nuse-1.5/.

Office of Science and Technology Policy (OSTP), 2022. Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People. [online] The White House. Available at: https://bidenwhitehouse.archives.gov/ostp/ai-bill-of-rights/.

Ouyang, L., et al., 2022. Training language models to follow instructions with human feedback. arXiv preprint. Available at: https://arxiv.org/abs/2203.02155.

1. As of May 2025, “On the Dangers of Stochastic Parrots” has been cited in 2,413 publications and downloaded 422,970 times. Its continued visibility is reflected in a dedicated Wikipedia page, and citation metrics are tracked through the ACM Digital Library.