Abstract
Large Language Models (LLMs) have lately been on the spotlight of researchers, businesses, and consumers alike. While the linguistic capabilities of such models have been studied extensively, there is growing interest in investigating them as cognitive subjects. In the present work, I examine GPT-3 and ChatGPT capabilities on an limited data inductive reasoning task from the cognitive science literature. The results suggest that these models’ cognitive judgements are not human like.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Data source: Google Trends (https://www.google.com/trends).
- 2.
References
Surging stockmarkets are powered by artificial intelligence (2023) The economist (June 10th 2023)
Bang Y, Cahyawijaya S, Lee N, Dai W, Su D, Wilie B, Lovenia H, Ji Z, Yu T, Chung W et al (2023) A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv:2302.04023
Bender EM, Gebru T, McMillan-Major A, Shmitchell S (2021) On the dangers of stochastic parrots: can language models be too big. In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 610–623
Binz M, Schulz E (2023) Using cognitive psychology to understand GPT-3. Proc Natl Acad Sci 120(6):e2218523120
Borji A (2023) A categorical archive of chatgpt failures. arXiv:2302.03494
Bubeck S, Chandrasekaran V, Eldan R, Gehrke J, Horvitz E, Kamar E, Lee P, Lee YT, Li Y, Lundberg S et al (2023) Sparks of artificial general intelligence: early experiments with GPT-4. arXiv:2303.12712
Cai ZG, Haslett DA, Duan X, Wang S, Pickering MJ (2023) Does chatgpt resemble humans in language use? arXiv:2303.08014
Chomsky N, Roberts I, Watumull J (2023) Noam Chomsky: the false promise of ChatGPT. The New York Times 8
Ettinger A (2020) What BERT is not: lessons from a new suite of psycholinguistic diagnostics for language models. Trans Assoc Comput Linguist 8:34–48
Floridi L (2023) AI as agency without intelligence: on ChatGPT, large language models, and other generative models. Philos & Technol 36(1):15
Freund L Exploring the intersection of rationality, reality, and theory of mind in AI reasoning: an analysis of GPT-4’s responses to paradoxes and tom tests
Griffiths TL, Tenenbaum JB (2006) Optimal predictions in everyday cognition. Psychol Sci 17(9):767–773
Gulordava K, Bojanowski P, Grave E, Linzen T, Baroni M (2018) Colorless green recurrent networks dream hierarchically. arXiv:1803.11138
Holterman B, van Deemter K (2023) Does chatgpt have theory of mind? arXiv:2305.14020
Katzir R (2023) Why large language models are poor theories of human linguistic cognition. A reply to Piantadosi (2023). Manuscript. Tel Aviv University. https://lingbuzz.net/lingbuzz/007190
Lipkin B, Wong L, Grand G, Tenenbaum JB (2023) Evaluating statistical language models as pragmatic reasoners. arXiv:2305.01020
Lloyd D (2023) What is it like to be a bot?: the world according to GPT-4. SSRN 4443727
Loconte R, Orrù G, Tribastone M, Pietrini P, Sartori G (2023) Challenging chatgpt ’intelligence’ with human tools: a neuropsychological investigation on prefrontal functioning of a large language model. Intelligence
Michaux C (2023) Can chat gpt be considered an author? i met with chat gpt and asked some questions about philosophy of art and philosophy of mind. SSRN 4439607
OpenAI. Chatgpt. https://chat.openai.com. May 24 Version
OpenAI. Gpt-3. https://platform.openai.com/docs/models/gpt-3. Model: davinci-003
Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A et al (2022) Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 35:27730–27744
Qian P, Levy RP (2019) Neural language models as psycholinguistic subjects: representations of syntactic state. Association for Computational Linguistics
Ribeiro MT, Wu T, Guestrin C, Singh S (2020) Beyond accuracy: behavioral testing of NLP models with checklist. arXiv:2005.04118
Scott AE, Neumann D, Niess J, Woźniak PW (2023) Do you mind? user perceptions of machine consciousness. In: Proceedings of the 2023 CHI conference on human factors in computing systems, pp. 1–19
Taecharungroj V (2023) “What can ChatGPT do?’’ analyzing early reactions to the innovative AI chatbot on twitter. Big Data Cogn Comput 7(1):35
Warstadt A, Singh A, Bowman SR (2019) Neural network acceptability judgments. Trans Assoc Comput Linguist 7:625–641
Xu Q, Peng Y, Wu M, Xiao F, Chodorow M, Li P (2023) Does conceptual representation require embodiment? insights from large language models. arXiv:2305.19103
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lamprinidis, S. (2024). LLM Cognitive Judgements Differ from Human. In: Farmanbar, M., Tzamtzi, M., Verma, A.K., Chakravorty, A. (eds) Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. FAIEMA 2023. Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications. Springer, Singapore. https://doi.org/10.1007/978-981-99-9836-4_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-9836-4_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9835-7
Online ISBN: 978-981-99-9836-4
eBook Packages: Business and ManagementBusiness and Management (R0)