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Aug. 27, 2024, 9:21 a.m.
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Understanding Limitations of Large Language Models in Letter and Syllable Recognition

According to powerful AI models like GPT-4o and Claude, the letter "r" appears twice in the word "strawberry. " However, these models, known as large language models (LLMs), have limitations in understanding concepts like letters and syllables because they don't have actual brains and rely on numerical representations of text. LLMs are built on transformers, which break text into tokens, but they don't comprehend individual letters efficiently. This issue is deeply embedded in the architecture of LLMs, making it challenging to fix. Tokenization methods and language complexities further complicate the matter.

Image generators, on the other hand, use diffusion models and encounter different challenges related to representing smaller details. OpenAI is developing a new AI product called Strawberry to enhance reasoning abilities, while Google DeepMind has introduced AlphaProof and AlphaGeometry 2 for formal math reasoning. These systems have demonstrated strong performance in problem-solving tasks.



Brief news summary

Large language models (LLMs) lack human-like thinking capabilities, as they struggle with concepts like letters and syllables. Unlike humans, LLMs process text by converting it into numbers, leading to difficulties in tasks like counting letters. Tokenization, which defines what constitutes a "word," presents another challenge, especially when dealing with multiple languages. Image generators using diffusion models excel at large objects but struggle with finer details like fingers and handwriting. Despite these issues, progress is being made, with improvements in image generators' representation of hands and LLMs aiming to enhance reasoning abilities. Google DeepMind has also showcased AI systems for solving complex math problems.
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