[1][2] The term was coined by Emily M. Bender[2][3] in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
🦜" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell (using the pseudonym "Shmargaret Shmitchell").
[5] The word "stochastic" – from the ancient Greek "stokhastikos" ('based on guesswork') – is a term from probability theory meaning "randomly determined".
[4] According to the machine learning professionals Lindholm, Wahlström, Lindsten, and Schön, the analogy highlights two vital limitations:[1][7] Lindholm et al. noted that, with poor quality datasets and other limitations, a learning machine might produce results that are "dangerously wrong".
[6] Its use expanded further when Sam Altman, CEO of Open AI, used the term ironically when he tweeted, "i am a stochastic parrot and so r u.
[6][16] The phrase is often referenced by some researchers to describe LLMs as pattern matchers that can generate plausible human-like text through their vast amount of training data, merely parroting in a stochastic fashion.
[19][20][18] That LLMs can’t distinguish fact and fiction leads to the claim that they can’t connect words to a comprehension of the world, as language should do.
[19][18] Further, LLMs often fail to decipher complex or ambiguous grammar cases that rely on understanding the meaning of language.
[19][18][4] One argument against the hypothesis that LLMs are stochastic parrot is their results on benchmarks for reasoning, common sense and language understanding.
[20][21] Such tests, and the smoothness of many LLM responses, help as many as 51% of AI professionals believe they can truly understand language with enough data, according to a 2022 survey.
However, the model frequently failed when tasked with logic and reasoning, especially when these prompts involved spatial awareness.
[17] The model’s varying quality of responses indicates that LLMs may have a form of "understanding" in certain categories of tasks while acting as a stochastic parrot in others.
[26] Models have shown examples of shortcut learning, which is when a system makes unrelated correlations within data instead of using human-like understanding.