Decoding Stumpers: Large Language Models vs. Human Problem-Solvers
This work provides insights into LLMs' cognitive abilities for AI researchers, though it is incremental as it builds on existing evaluations.
The paper compared four state-of-the-art LLMs to humans on solving stumpers, finding that new-generation LLMs excel and surpass human performance, while humans are better at verifying solutions.
This paper investigates the problem-solving capabilities of Large Language Models (LLMs) by evaluating their performance on stumpers, unique single-step intuition problems that pose challenges for human solvers but are easily verifiable. We compare the performance of four state-of-the-art LLMs (Davinci-2, Davinci-3, GPT-3.5-Turbo, GPT-4) to human participants. Our findings reveal that the new-generation LLMs excel in solving stumpers and surpass human performance. However, humans exhibit superior skills in verifying solutions to the same problems. This research enhances our understanding of LLMs' cognitive abilities and provides insights for enhancing their problem-solving potential across various domains.