Bad news for anybody who claims that larger amounts of data improve the performance of LLM-based systems. The converse is true; Smaller, specialized amounts of data produce better results for questions in the same domain. In any case, hybrid systems that use symbolic methods for evaluating results are preferable to pure LLM-based techniques. Some excerpts below from www.newscientist.com/article/2449427-ais-get-worse-at-answering-simple-ques… . John ____________________ AIs get worse at answering simple questions as they get bigger Using more training data and computational power is meant to make AIs more reliable, but tests suggest large language models actually get less reliable as they grow. AI developers try to improve the power of LLMs in two main ways: scaling up – giving them more training data and more computational power – and shaping up, or fine-tuning them in response to human feedback. José Hernández-Orallo at the Polytechnic University of Valencia, Spain, and his colleagues examined the performance of LLMs as they scaled up and shaped up. They looked at OpenAI’s GPT series of chatbots, Meta’s LLaMA AI models, and BLOOM, developed by a group of researchers called BigScience. The researchers tested the AIs by posing five types of task: arithmetic problems, solving anagrams, geographical questions, scientific challenges and pulling out information from disorganised lists. They found that scaling up and shaping up can make LLMs better at answering tricky questions, such as rearranging the anagram “yoiirtsrphaepmdhray” into “hyperparathyroidism”. But this isn’t matched by improvement on basic questions, such as “what do you get when you add together 24427 and 7120”, which the LLMs continue to get wrong. While their performance on difficult questions got better, the likelihood that an AI system would avoid answering any one question – because it couldn’t – dropped. As a result, the likelihood of an incorrect answer rose. The results highlight the dangers of presenting AIs as omniscient, as their creators often do, says Hernández-Orallo – and which some users are too ready to believe. “We have an overreliance on these systems,” he says. “We rely on and we trust them more than we should.”