Patrick Lewis, Director of Machine Learning at Cohere and a leading voice in the global AI research community, is redefining how large language models (LLMs) learn and apply knowledge. Known for his pioneering work on retrieval-augmented generation (RAG), Lewis is spearheading research that enhances model efficiency, scalability, and trustworthiness—key challenges as the next generation of AI unfolds.

Academic Roots and the Journey into AI

Lewis began his academic journey at the University of Cambridge, where he completed a Master’s in Natural Sciences. He later earned a Ph.D. in Artificial Intelligence from University College London (UCL) under the mentorship of renowned researchers Sebastian Riedel and Pontus Stenetorp. His doctoral thesis, titled “Improving Neural Question Answering with Retrieval and Generation”, tackled core challenges in natural language understanding—specifically, how to optimize QA models by minimizing their dependence on large annotated datasets while maintaining robustness and speed.

Following his Ph.D., Lewis joined Meta AI’s Fundamental AI Research (FAIR) Lab, where he played a central role in the development of innovative approaches to retrieval-augmented neural systems. Today at Cohere, he leads global research teams focused on integrating retrieval and tool-use mechanisms into next-gen models.

Leading the Charge in Retrieval-Augmented Generation

Retrieval-augmented generation, a subfield within NLP that fuses information retrieval systems with generative models, has become a cornerstone of Lewis’s work. His research seeks to make LLMs more adaptable, updatable, and factually grounded by leveraging external knowledge rather than solely relying on pre-trained weights.

At Cohere, Lewis is developing systems that dynamically retrieve relevant data from vast corpora during inference—reducing hallucinations and computational burden while boosting transparency and explainability. His ultimate aim: to build universal models that excel across diverse, knowledge-intensive tasks like fact-checking, long-form QA, and instruction following.

Notable Contributions to the NLP Field

Lewis’s academic and industry contributions have yielded influential papers and models that set benchmarks across the AI community:

• “Atlas: Few-shot Learning with Retrieval-Augmented Language Models” (2022) – A foundational work showing how retrieval systems can drastically improve few-shot learning outcomes.
• “PAQ: 65 Million Probably-Asked Questions” – Introduced a massive dataset for open-domain QA, pushing the envelope in large-scale training efficiency.
• “Question and Answer Test-Train Overlap in Open-Domain QA” – Provided a critical lens on dataset biases and their effects on model evaluation.
• “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” – Explored RAG’s potential to enhance long-context and factual reasoning in LLMs.

His recent work, including “Task-Aware Retrieval with Instructions”, showcases how retrieval systems can be fine-tuned with instruction prompts, achieving high zero-shot accuracy without parameter updates. Another standout publication, “Mini-Model Adaptation”, proposes using lightweight modules to extend large models into new languages with minimal compute—a game-changing development for cross-lingual AI.

Tackling AI Safety and Toxicity with Innovation

Lewis is also pioneering methods to make AI safer and more ethically aligned. His 2024 study, “Goodtriever”, outlines a new framework for reducing toxicity in generated text using adaptive retrieval during inference. The approach significantly cuts latency while preserving safety standards—an increasingly important focus as LLMs power consumer-facing apps.

A Vision for the Future of NLP

Patrick Lewis’s research agenda is firmly rooted in practical application. By marrying retrieval efficiency with linguistic comprehension, he is addressing a central limitation of many LLMs: static, uneditable knowledge. His vision includes models that are modular, updateable in real-time, and context-aware—traits that will define next-generation AI tools in sectors such as healthcare, education, and law.

At Cohere, Lewis continues to push the boundaries of machine learning with a research ethos that emphasizes transparency, scalability, and human-aligned performance. His work is not only improving how language models process information but also shaping how they responsibly interact with the world.

Conclusion

Patrick Lewis has emerged as one of the foremost researchers driving meaningful progress in AI and NLP. Through groundbreaking work at Cohere and a legacy of influential publications, he is building systems that go beyond generative capabilities—toward models that retrieve, reason, and adapt with intelligence. As artificial intelligence continues to evolve, Lewis’s research will undoubtedly remain at the forefront of innovation and impact.