Machine Learning Street Talk (MLST)
#100 Dr. PATRICK LEWIS (co:here) - Retrieval Augmented Generation
Feb 10, 2023
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.
26:28
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Quick takeaways
- Retrieval-Augmented Generation combines retrieval and generation steps to produce verifiable and grounded outputs, allowing the language model to generate claims and provide evidence to back them up.
- Fusing information across documents in Retrieval-Augmented Generation enhances interpretability, updateability, and facilitates parallel processing, addressing the challenge of accommodating long sequences in language models.
Deep dives
Retrieval-Augmented Generation
Dr. Patrick Lewis discusses the concept of Retrieval-Augmented Generation (RAG), a model that learns to retrieve and generate text based on the retrieved information. RAG combines retrieval and generation steps to produce more verifiable and grounded outputs. By using citation, RAG provides artifacts from the real world that support the generated claims. It allows the language model to generate claims and provide evidence to back them up. RAG can be trained jointly, with the retriever system retrieving relevant documents and the language model learning to generate outputs based on both the input and retrieved information. This approach has potential for information-seeking dialogue, fact-checking, and question-answering tasks.
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