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Strong fluctuations across AIW problem variations. Also for higher performers, eg GPT-4o, GPT-4 and Claude Opus 3, correct response rates vary strongly from close to 1 to close to 0, despite only slight changes introduced in AIW variations (a color per each variation 1–4). This clearly shows lack of model robustness, hinting basic reasoning deficits. Credit: arXiv (2024). DOI: 10.48550/arxiv.2406.02061
Even the best AI large language models (LLMs) fail dramatically when it comes to simple logical questions. This is the conclusion of researchers from the Jülich Supercomputing Center (JSC), the School of Electrical and Electronic Engineering at the University of Bristol and the LAION AI laboratory.
In their paper posted to the arXiv preprint server, titled “Alice in Wonderland: S...
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