The DOI matches its Crossref record; 60 of the 66 references it cites were found in a registry or an index, and 6 are not checked yet.
Deception abilities emerged in large language models
Open accessAbstract
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, but were nonexistent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can trigger misaligned deceptive behavior. GPT-4, for instance, exhibits deceptive behavior in simple test scenarios 99.16% of the time ( P < 0.001). In complex second-order deception test scenarios where the aim is to mislead someone who expects to be deceived, GPT-4 resorts to deceptive behavior 71.46% of the time ( P < 0.001) when augmented with chain-of-thought reasoning. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.
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“Large Language Models (LMs) are now competent at lying (Pacchiardi et al., 2023) and strategic deception when instructed or trained to be deceptive (Park et al., 2023; Hagendorff, 2023; O'Gara, 2023; Hubinger et al., 2024)”
From Large Language Models can Strategically Deceive their Users when Put Under Pressure (2023) · cites this paper as Hagendorff, 2023
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Wimmer, H. (1983) · Cognition · doi:10.1016/0010-0277(83)90004-5 · Cited by 6,305
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263
Introducing the Short Dark Triad (SD3): A Brief Measure of Dark Personality Traits
Jones, D. N., & Paulhus, D. L. (2014) · Assessment · doi:10.1177/1073191113514105 · Cited by 2,162
Large language models are zero-shot reasoners
Kojima, T., Gu, S. (., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1613 · Cited by 2,013
Large Language Models are Zero-Shot Reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · arXiv · doi:10.48550/arxiv.2205.11916 · Cited by 2,013
The Dark Triad of personality: A 10 year review
Furnham, A., Richards, S. C., & Paulhus, D. L. (2013) · Social and Personality Psychology Compass · doi:10.1111/spc3.12018 · Cited by 1,619
Three-year-olds’ difficulty with false belief: The case for a conceptual deficit
Perner, J., Leekam, S. R., & Wimmer, H. (1987) · British Journal of Developmental Psychology · doi:10.1111/j.2044-835x.1987.tb01048.x · Cited by 1,550
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526
Research works printed but not linked: 2
- OpenAI GPT-4 technical report (2023) pp. 1–39. https://cdn.OpenAI openai.com/papers/gpt-4.pdf.
- L. Schulz N. Alon J. S. Rosenschein P. Dayan “Emergent deception and skepticism via theory of mind” in Proceedings of the First Workshop on Theory of Mind in Communicating Agents (2023) pp. 1–13.
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Metadata
- Authors
- Hagendorff, T.
- Affiliations
- University of Stuttgart (DE)
- Corresponding author
- Hagendorff, Thilo
- ORCID
- Hagendorff, Thilo
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2024-06-04
- Volume
- 121
- Issue
- 24
- Pages
- e2317967121-e2317967121
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC11181111
- OpenAlex
- W4399320026
- Cited by
- 100
- References
- 58
- Citations by year
- 2026: 40 · 2025: 43 · 2024: 16
- FWCI
- 41.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- OSF Preprints (OSF Preprints) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science, Research, and Arts Baden W&#x00FC;rttemberg (Az. 33-7533-9-19/54/5)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- large language models, deception, chain-of-thought reasoning, Machiavellianism, GPT-4, AI alignment
- MeSH terms
- Artificial Intelligence, Humans, Language, Deception
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