AIs get worse at answering simple questions as they get bigger

Large language models (LLMs) seem to get less reliable at answering simple questions when they get bigger and learn from human feedback. 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. How does ChatGPT work and do AI-powered chatbots "think" like us? 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.