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Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.
rtificial intelligence seems more powerful than ever, with chatbots like Bard and ChatGPT capable of producing uncannily humanlike text. But for all their talents, these bots still leave researchers wondering: Do such models actually understand what they are saying? “Clearly, some people believe they do,” said the AI pioneer Geoff Hinton in a recent conversation with Andrew Ng, “and some people believe they are just stochastic parrots.”
This evocative phrase comes from a 2021 paper co-authored by Emily Bender, a computational linguist at the University of Washington. It suggests that large language models (LLMs) — which form the basis of modern chatbots — generate text only by combining information they have already seen “without any reference to meaning,” the authors wrote, which makes an LLM “a stochastic parrot.”
These models power many of today’s biggest and best chatbots, so Hinton argued that it’s time to determine the extent of what they understand. The question, to him, is more than academic. “So long as we have those differences” of opinion, he said to Ng, “we are not going to be able to come to a consensus about dangers.”
New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.
www.quantamagazine.org
rtificial intelligence seems more powerful than ever, with chatbots like Bard and ChatGPT capable of producing uncannily humanlike text. But for all their talents, these bots still leave researchers wondering: Do such models actually understand what they are saying? “Clearly, some people believe they do,” said the AI pioneer Geoff Hinton in a recent conversation with Andrew Ng, “and some people believe they are just stochastic parrots.”
This evocative phrase comes from a 2021 paper co-authored by Emily Bender, a computational linguist at the University of Washington. It suggests that large language models (LLMs) — which form the basis of modern chatbots — generate text only by combining information they have already seen “without any reference to meaning,” the authors wrote, which makes an LLM “a stochastic parrot.”
These models power many of today’s biggest and best chatbots, so Hinton argued that it’s time to determine the extent of what they understand. The question, to him, is more than academic. “So long as we have those differences” of opinion, he said to Ng, “we are not going to be able to come to a consensus about dangers.”
New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.
New Theory Suggests Chatbots Can Understand Text | Quanta Magazine
Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.
www.quantamagazine.org
