Quoc V. Le is a Vietnamese-born computer scientist and one of the founding members of Google Brain. He has been an author on a remarkable run of foundational language papers: the 2014 “Sequence to Sequence Learning with Neural Networks” with Ilya Sutskever and Oriol Vinyals, the work behind Google’s neural machine translation system, and the 2021 FLAN paper “Finetuned Language Models Are Zero-Shot Learners,” which showed that instruction tuning - finetuning a model on many tasks phrased as natural-language instructions - lets a model follow instructions for tasks it never saw in training.
Le was also a co-author on “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” which demonstrated that prompting a model to show its intermediate reasoning steps sharply improves performance on arithmetic and commonsense problems. Earlier in his career he worked on large-scale unsupervised learning, including the 2012 Google Brain experiment in which a network learned to recognize cats from unlabeled YouTube frames.
Why business readers should care: Instruction tuning and chain-of-thought prompting are two of the techniques that turned raw language models into the helpful, instruction-following assistants now used in products. Le’s name sits on the original papers for both.