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Therefore, they are labeled to be mere "stochastic parrots". [4] According to the machine learning professionals Lindholm, Wahlström, Lindsten, and Schön, the analogy highlights two vital limitations: [1] [7] LLMs are limited by the data they are trained by and are simply stochastically repeating contents of datasets.
In contrast, some skeptics of LLM understanding believe that existing LLMs are "simply remixing and recombining existing writing", [116] a phenomenon known as stochastic parrot, or they point to the deficits existing LLMs continue to have in prediction skills, reasoning skills, agency, and explainability. [111]
English: The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size.
In 2021, Bender presented a paper, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" co-authored with Google researcher Timnit Gebru and others at the ACM Conference on Fairness, Accountability, and Transparency [21] that Google tried to block from publication, part of a sequence of events leading to Gebru departing ...
Gebru had coauthored a paper on the risks of large language models (LLMs) acting as stochastic parrots, and submitted it for publication. According to Jeff Dean, the paper was submitted without waiting for Google's internal review, which then concluded that it ignored too much relevant research. Google management requested that Gebru either ...
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This page in a nutshell: Avoid using large language models (LLMs) to write original content or generate references. LLMs can be used for certain tasks (like copyediting, summarization, and paraphrasing) if the editor has substantial prior experience in the intended task and rigorously scrutinizes the results before publishing them.
However, they highlight that the evidence for LLMs having action dispositions necessary for belief-desire psychology remains inconclusive. Additionally, they refute common skeptical challenges, such as the " stochastic parrots " argument and concerns over memorization, asserting that LLMs exhibit structured internal representations that align ...