Google & Stanford Team Applies Chain-of-Thought Prompting to Surpass Human Performance on Challenging BIG-Bench Tasks

In the new paper Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them, a Google Research and Stanford University team applies chain-of-thought (CoT) prompting — a series of intermediate reasoning steps — to 23 BIG-Bench tasks on which language models have failed to outperform the average human rater. The proposed approach enables models to surpass human performance on 17 of the 23 tasks.

ai artificial intelligence big deep-neural-networks google human human performance language model machine learning machine learning & data science ml performance research stanford team technology