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Recent research on Large Language Models (LLMs) highlights their capabilities and limitations. Studies show that LLMs like GPT-4 outperform previous versions in coding assignments and reasoning tasks. While LLMs have shown potential in scientific discovery and education, they still struggle with unconventional physical reasoning tests. The use of prompt engineering can enhance LLM performance, indicating their growing potential in various fields.
Re-upping a piece from last year by @hamandcheese on LLMs and language meaning: “I see the success of LLMs as vindicating the use theory of meaning, especially when contrasted with the failure of symbolic approaches to natural language processing.” https://t.co/5hVWEU5bc7
.@TrentonBricken explains how we know LLMs are actually generalizing - aka they're not just stochastic parrots: - Training models on code makes them better at reasoning in language. - Models fine tuned on math problems become better at entity detection. - We can just… https://t.co/1PfAIMyXsa
Students' soaring use of AI tools has gotten intense attention lately, in part due to widespread accusations of cheating. But a recent poll found that more teachers use generative AI than students. https://t.co/NpoIbmgaWX