{"slug": "potemkin-understanding-in-large-language-models-2025", "title": "Potemkin Understanding in Large Language Models (2025)", "summary": "A paper posted to arXiv on June 26, 2025 introduces a formal framework for judging whether large language model benchmark performance reflects genuine understanding, arguing that benchmarks such as AP exams are valid tests only if LLMs misunderstand concepts the way humans do. The authors, including Marina Mancoridis, present two procedures for quantifying these \"potemkin\" understandings — one using a purpose-built benchmark across three domains and one general procedure giving a lower bound on their prevalence — and report that potemkins are ubiquitous across models, tasks, and domains. The paper states these failures reflect deeper internal incoherence in concept representations, not merely incorrect understanding.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 26 Jun 2025 (\n\n[v1](https://arxiv.org/abs/2506.21521v1)), last revised 29 Jun 2025 (this version, v2)]\n# Title:Potemkin Understanding in Large Language Models\n\n[View PDF](https://arxiv.org/pdf/2506.21521)\n\n[HTML (experimental)](https://arxiv.org/html/2506.21521v2)\n\nAbstract:Large language models (LLMs) are regularly evaluated using benchmark datasets. But what justifies making inferences about an LLM's capabilities based on its answers to a curated set of questions? This paper first introduces a formal framework to address this question. The key is to note that the benchmarks used to test LLMs -- such as AP exams -- are also those used to test people. However, this raises an implication: these benchmarks are only valid tests if LLMs misunderstand concepts in ways that mirror human misunderstandings. Otherwise, success on benchmarks only demonstrates potemkin understanding: the illusion of understanding driven by answers irreconcilable with how any human would interpret a concept. We present two procedures for quantifying the existence of potemkins: one using a specially designed benchmark in three domains, the other using a general procedure that provides a lower-bound on their prevalence. We find that potemkins are ubiquitous across models, tasks, and domains. We also find that these failures reflect not just incorrect understanding, but deeper internal incoherence in concept representations.\n    \n\n## Submission history\n\nFrom: Marina Mancoridis [\n[view email](https://arxiv.org/show-email/15d8ef36/2506.21521)]\n\n**Thu, 26 Jun 2025 17:41:35 UTC (2,403 KB)**\n\n[\\[v1\\]](https://arxiv.org/abs/2506.21521v1)\n**[v2]** Sun, 29 Jun 2025 18:12:45 UTC (2,402 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/potemkin-understanding-in-large-language-models-2025", "canonical_source": "https://arxiv.org/abs/2506.21521", "published_at": "2026-09-16 07:45:04+00:00", "updated_at": "2026-09-16 08:14:37.397498+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "artificial-intelligence", "natural-language-processing"], "entities": ["arXiv", "Marina Mancoridis", "Potemkin Understanding in Large Language Models"], "alternates": {"html": "https://wpnews.pro/news/potemkin-understanding-in-large-language-models-2025", "markdown": "https://wpnews.pro/news/potemkin-understanding-in-large-language-models-2025.md", "text": "https://wpnews.pro/news/potemkin-understanding-in-large-language-models-2025.txt", "jsonld": "https://wpnews.pro/news/potemkin-understanding-in-large-language-models-2025.jsonld"}}