Patrick Lewis

Dr. Patrick Lewis, a leading expert and coiner of Retrieval Augmented Generation (RAG), discusses the evolution of language models and the challenges in evaluating RAG systems. He highlights the importance of data quality and human-AI collaboration, while also delving into dense vs. sparse retrieval methods. Further, Patrick shares insights on striking a balance between faithfulness and fluency in RAG applications and the complexities of user interface design for AI tools. His journey from chemistry to AI research adds a unique flair to the conversation.

Dr. Patrick Lewis, an AI and NLP Research Scientist at co:here, delves into the cutting-edge world of Retrieval-Augmented Language Models. He discusses the limitations of existing transformer models in handling large inputs, revealing the need for better techniques. The conversation highlights the importance of enhancing verifiability in language models by integrating credible sources. Patrick also explores the complexities of information retrieval in improving contextual relevance, using the innovative Atlas project as a prime example.