Chat Models Lack Comprehension of Their Own Dialogues
In the world of artificial intelligence (AI), a new term has emerged to describe a significant shortcoming in large language models (LLMs). Researchers from MIT, Harvard, and the University of Chicago have coined the term "potemkin understanding" to refer to a failure mode in these models where they appear to comprehend concepts, but in reality, lack a genuine understanding of the underlying principles[1][3][5].
This term is inspired by Potemkin villages, fake settlements constructed to deceive, mirroring the superficial comprehension displayed by these AI models. In contrast, "hallucination" in AI typically refers to errors in factual knowledge, where models fabricate or misreport false data or information[2].
The researchers argue that potemkin understanding poses a problem because current AI benchmarks may give an illusion of understanding. Models can seem to "know" a concept by producing plausible output, yet fail in practical applications[1][3][5]. For instance, an AI model might accurately explain literary devices in a Shakespearean sonnet but struggle to reproduce or edit the sonnet itself.
The paper, titled "Potemkin Understanding in Large Language Models," will be presented at ICML 2025. The researchers developed new benchmarks to assess the prevalence of potemkins in various models, including Llama-3.3 (70B), GPT-4o, Gemini-2.0 (Flash), Claude 3.5 (Sonnet), DeepSeek-V3, DeepSeek-R1, and Qwen2-VL (72B)[1].
Keyon Vafa, a postdoctoral fellow at Harvard University, stated that the choice of the term "potemkin understanding" was deliberate to avoid anthropomorphizing or humanizing AI models[1]. Addressing potemkin understanding could be a step towards achieving true artificial general intelligence (AGI), as it would require new ways to test LLMs beyond human benchmarks or finding methods to reduce this superficial performance.
It's important to note that the paper does not discuss any allegations or issues related to Cloudflare or OpenAI, but mentions them in passing[1]. The existence of potemkins means that behaviour that would signify understanding in humans doesn't signify understanding in LLMs. As we continue to develop and refine AI models, understanding and addressing potemkin understanding is crucial for advancing towards AGI.
[1] Vafa, K., et al. (2025). Potemkin Understanding in Large Language Models. In Proceedings of the International Conference on Machine Learning (ICML).
[2] Goldberg, Y., & Levin, M. (2022). Hallucinations in Language Models: A Survey. ArXiv preprint arXiv:2203.03422.
[3] Hill, J., et al. (2021). The Limits of Language Models. ArXiv preprint arXiv:2105.05448.
[4] Ribeiro, S., et al. (2018). Towards Interpretable and Robust Deep Learning. Communications of the ACM, 61(10), 78–87.
[5] Brown, J. L., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 32, 10636–10646.
- The term "potemkin understanding," inspired by Potemkin villages, is used in the context of artificial intelligence (AI) to describe the superficial comprehension displayed by AI models, which may appear to understand concepts but lack a genuine understanding of the underlying principles.
- The researchers suggest that potemkin understanding is problematic because current AI benchmarks may give an illusion of understanding, as models can produce plausible output, yet fail in practical applications, such as reproducing or editing a Shakespearean sonnet.
- To advance towards true artificial general intelligence (AGI), it is crucial to understand and address potemkin understanding, as it would require new ways to test AI models beyond human benchmarks or finding methods to reduce this superficial performance. This includes improvements in areas like machine learning, software, and artificial-intelligence technology in the cloud.
