Large Language Models Struggle with Basic Math Comparisons
Recent reports indicate that several prominent large language models (LLMs) fail to correctly answer simple elementary math questions, such as determining whether 9.11 or 9.9 is larger. According to Chinese media outlet Yicai, advanced AI systems including OpenAI’s GPT-4o, Moonshot’s Kimi, and ByteDance’s Doubao initially provided incorrect answers to this comparison. In contrast, chatbots developed by Chinese tech giants Baidu and Tencent successfully generated the correct response, albeit through different logical methods. Baidu’s model compared fractional parts after verifying identical integer values, while Tencent’s Hunyuan calculated that subtracting 9.9 from 9.11 yields a negative result. Interestingly, models like ChatGPT and Kimi corrected their errors only after users explicitly clarified that the question pertained to numerical value rather than version numbering. This incident highlights a significant limitation in current AI architectures, which are primarily trained on internet data for natural human conversation and text-based knowledge tasks, often leading to confusion when interpreting numeric strings that resemble software versions or dates.
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Large Language Models Struggle with Basic Math Comparisons
Recent reports indicate that several prominent large language models (LLMs) fail to correctly answer simple elementary math questions, such as determining whether 9.11 or 9.9 is larger. According to Chinese media outlet Yicai, advanced AI systems including OpenAI’s GPT-4o, Moonshot’s Kimi, and ByteDance’s Doubao initially provided incorrect answers to this comparison. In contrast, chatbots developed by Chinese tech giants Baidu and Tencent successfully generated the correct response, albeit through different logical methods. Baidu’s model compared fractional parts after verifying identical integer values, while Tencent’s Hunyuan calculated that subtracting 9.9 from 9.11 yields a negative result. Interestingly, models like ChatGPT and Kimi corrected their errors only after users explicitly clarified that the question pertained to numerical value rather than version numbering. This incident highlights a significant limitation in current AI architectures, which are primarily trained on internet data for natural human conversation and text-based knowledge tasks, often leading to confusion when interpreting numeric strings that resemble software versions or dates.
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