Dr. Patrick Lewis, a pioneer in Retrieval Augmented Generation at Cohere, dives deep into RAG systems and their evolution. He shares insights on the challenges of evaluating AI models and the critical role of data quality in training. The conversation touches on dense vs. sparse retrieval methods, emphasizing the benefits of hybrid approaches. Patrick also discusses his unique journey from a synthetic chemist to AI research, the importance of human-AI collaboration, and the design challenges for effective AI-augmented research tools.

Dr. Patrick Lewis is a London-based AI and Natural Language Processing Research Scientist, working at co:here. Prior to this, Patrick worked as a research scientist at the Fundamental AI Research Lab (FAIR) at Meta AI. During his PhD, Patrick split his time between FAIR and University College London, working with Sebastian Riedel and Pontus Stenetorp.  Patrick’s research focuses on the intersection of information retrieval techniques (IR) and large language models (LLMs). He has done extensive work on Retrieval-Augmented Language Models. His current focus is on building more powerful, efficient, robust, and update-able models that can perform well on a wide range of NLP tasks, but also excel on knowledge-intensive NLP tasks such as Question Answering and Fact Checking. YT version: https://youtu.be/Dm5sfALoL1Y MLST Discord: https://discord.gg/aNPkGUQtc5 Support us! https://www.patreon.com/mlst References: Patrick Lewis (Natural Language Processing Research Scientist @ co:here) https://www.patricklewis.io/ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Patrick Lewis et al) https://arxiv.org/abs/2005.11401 Atlas: Few-shot Learning with Retrieval Augmented Language Models (Gautier Izacard, Patrick Lewis, et al) https://arxiv.org/abs/2208.03299 Improving language models by retrieving from trillions of tokens (RETRO) (Sebastian Borgeaud et al) https://arxiv.org/abs/2112.04426