Computer Science > Artificial Intelligence
arXiv:2308.14337 (cs)
[Submitted on 28 Aug 2023]
Title:Cognitive Effects in Large Language Models
View a PDF of the paper titled Cognitive Effects in Large Language Models, by Jonathan Shaki and 2 other authors
View PDFAbstract:Large Language Models (LLMs) such as ChatGPT have received enormous attention over the past year and are now used by hundreds of millions of people every day. The rapid adoption of this technology naturally raises questions about the possible biases such models might exhibit. In this work, we tested one of these models (GPT-3) on a range of cognitive effects, which are systematic patterns that are usually found in human cognitive tasks. We found that LLMs are indeed prone to several human cognitive effects. Specifically, we show that the priming, distance, SNARC, and size congruity effects were presented with GPT-3, while the anchoring effect is absent. We describe our methodology, and specifically the way we converted real-world experiments to text-based experiments. Finally, we speculate on the possible reasons why GPT-3 exhibits these effects and discuss whether they are imitated or reinvented.
| Comments: | Accepted and will be published in the ECAI conference |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Report number: | ECAI 2023 |
| Cite as: | arXiv:2308.14337 [cs.AI] |
| (orarXiv:2308.14337v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2308.14337 arXiv-issued DOI via DataCite | |
| Journal reference: | ECAI 2023 pages 2105-2112 |
| Related DOI: | https://doi.org/10.3233/FAIA230505 DOI(s) linking to related resources |
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View a PDF of the paper titled Cognitive Effects in Large Language Models, by Jonathan Shaki and 2 other authors
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