Large language models are powerful imitators, but not innovators


According to a psychological study, large AI models such as OpenAI’s ChatGPT are strong imitators that mimic human content but are not innovative.

In their study, published in the journal Perspectives on Psychological Science, researchers Eunice Yiu, Eliza Kosoy, and Alison Gopnik emphasize that LLMs efficiently mimic existing human content by extracting patterns from large amounts of data and generating new responses.

LLMs are powerful imitators, they say, comparable to historical technologies such as language, writing, printing, libraries and the Internet that have fostered cultural transfer and evolution.

However, they are not focused on truth-seeking, as human perceptual or action systems are, but on extracting and transferring existing knowledge, often without a deep understanding of the causal or relational aspects of the information being processed.



Limitations of AI innovation.

The study also explores the concept of innovation, suggesting that LLMs lack the capacity for innovation found in young children. The authors used tool use and innovation as a point of comparison.

They found that while AI models could replicate familiar tool uses, they had difficulty in tasks that required inventing new tool uses or discovering new causal relationships.

Tests of tool use and causal relationships found that AI models had difficulty coming up with innovative solutions to problems compared to children. The models were able to identify superficial commonalities between objects, but were less capable when it came to selecting a new functional tool for problem-solving.

The study involved the use of a virtual “Blicket detector,” a machine that responds to certain objects with light and music, while remaining quiet for other objects. The researchers found that young children were able to figure out the causal structure of the machine with just a few observations.

In contrast, LLMs such as OpenAI’s ChatGPT, Google’s PaLM, and LaMDA failed to derive the correct causal overhypotheses from the data. Despite their extensive training, LLMs were unable to generate the relevant causal structures compared to children. According to the research team, this demonstrates their limitations in deriving inferences about novel events or relationships.


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