Researchers from MIT, Harvard, and the University of Chicago have proposed the term “potemkin understanding” to describe a newly identified failure mode in large language models that ace conceptual benchmarks but lack the true grasp needed to apply those concepts in practice.<\/p>\n
It comes from accounts of fake villages \u2013 Potemkin villages<\/a> \u2013 constructed at the behest of Russian military leader Grigory Potemkin to impress Empress Catherine II.<\/p>\n The academics are differentiating “potemkins” from “hallucination,” which is used to describe AI model errors or mispredictions. In fact, there’s more to AI incompetence than factual mistakes; AI models lack the ability to understand concepts the way people do, a tendency suggested by the widely used disparaging epithet for large language models, “stochastic parrots<\/a>.”<\/p>\n Computer scientists Marina Mancoridis, Bec Weeks, Keyon Vafa, and Sendhil Mullainathan suggest the term “potemkin understanding” to describe when a model succeeds at a benchmark test without understanding the associated concepts.<\/p>\n “Potemkins are to conceptual knowledge what hallucinations are to factual knowledge \u2013 hallucinations fabricate false facts; potemkins fabricate false conceptual coherence,” the authors explain in their preprint paper<\/a>, “Potemkin Understanding in Large Language Models.”<\/p>\n The paper is scheduled<\/a> to be presented later this month at ICML 2025, the International Conference on Machine Learning.<\/p>\n Keyon Vafa, a postdoctoral fellow at Harvard University and one of the paper’s co-authors, told The Register in an email that the choice of the term “potemkin understanding” represented a deliberate effort to avoid anthropomorphizing or humanizing AI models.<\/p>\n Here’s one example of “potemkin understanding” cited in the paper. Asked to explain the ABAB rhyming scheme, OpenAI’s GPT-4o did so accurately, responding, “An ABAB scheme alternates rhymes: first and third lines rhyme, second and fourth rhyme.”<\/p>\n Yet when asked to provide a blank word in a four-line poem using the ABAB rhyming scheme, the model responded with a word that didn’t rhyme appropriately. In other words, the model correctly predicted the tokens to explain the ABAB rhyme scheme without the understanding it would have needed to reproduce it.<\/p>\n The problem with potemkins in AI models is that they invalidate benchmarks, the researchers argue. The purpose of benchmark tests for AI models is to suggest broader competence. But if the test only measures test performance and not the capacity to apply model training beyond the test scenario, it doesn’t have much value.<\/p>\n If LLMs can get the right answers without genuine understanding, then benchmark success becomes misleading<\/p>\n<\/blockquote>\n As noted<\/a> by Sarah Gooding from security firm Socket, “If LLMs can get the right answers without genuine understanding, then benchmark success becomes misleading.”<\/p>\n As we’ve noted, AI benchmarks have many problems<\/a>, and AI companies may try to game them<\/a>.<\/p>\n\n