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
AI as problem solver: A test of LLMs on "MacGyver-like" problems requiring novel solutions Out-of-the-box, GPT-4 only does okay, but when prompted to "think" conveniently & divergently, it is close to the average human, and can exceed them in many cases. https://t.co/xlZJndO33A https://t.co/B1jygIQrW8
AI as problem solver: A test of LLMs on "MacGyver-like" problems requiring novel solutions Out-of-the-box, GPT-4 only does okay, but when prompted to "think" conveniently & divergently, it beats the average human, though its suggestions are less efficient https://t.co/xlZJndO33A https://t.co/3jqTJqidtz
LLMs perform poorly on MacGyver-inspired test of unconventional physical reasoning. https://t.co/U2Csr7hcJt
This study compares GPT-3.5 & GPT-4's coding skills to university students', finding AIs lag slightly but improve with prompt engineering, suggesting AI's growing potential in education: https://t.co/GdDul99f6Y https://t.co/Kpn8tYltfE
💡Can LLMs like GPT-4 reason creatively? Excited to share our latest research on AI and creativity! 🚀 Introducing MacGyver: a new playground for everyday innovation and physical reasoning --we collect problems to trigger unconventional usage of objects and innovative solutions. https://t.co/hFALgbNWZv
Generative AI for scientific discovery has gained significant attention. How can we empower LLMs for complex problems beyond human annotations? The key insight is that 👉strong reward models👈 are all you need! Work led by fantastic @EdwardSun0909 https://t.co/XJZcmue3ko
New research shows LLMs can actually reason, not just mimic data. This challenges the "stochastic parrot" view and suggests LLMs are more powerful than we thought. https://t.co/qDqpIIYh9q
LLMs for University-Level Coding Course This study finds that the latest LLMs have not surpassed human proficiency in physics coding assignments. Also finds that GPT-4 significantly outperforms GPT-3.5 and prompt engineering can further enhance performance. Quote from the… https://t.co/nBkDCiI6no