llm
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LLMs Are Becoming More Similar—to Each Other and to Us
Recently, I read this paper “The Platonic Representation Hypothesis” (Huh et al. 2024). The hypothesis states that all deep neural networks–no matter how they are trained, what training dataset they use, or which modalities they operate on–are converging to one shared statistical model of reality in their representation spaces. This hypothesis is powerful in that Continue reading
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Teach AI to Think in Math
In our previous discussion on Why LLMs Aren’t Good at Math, we explored some limitations of large language models (LLMs) in mathematical reasoning. But what exactly makes math different from natural language skills like reading, writing, listening, and speaking? One insight is that “the essence of math is about symmetry.” Here, symmetry goes beyond just Continue reading
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Why LLMs Aren’t Good at Math
Large language models (LLMs) are trained by processing huge amounts of text. They “learn” by reading, much like people do through reading and listening. When kids develop thinking skills, they first learn language, and only much later—around third grade or so—do they begin to understand math and logical reasoning. Thinking in precise, step-by-step ways is Continue reading
Sida Liu 