Edge 305: In-Context Retrieval-Augmented Language Models
Was this email forwarded to you? Sign up here Edge 305: In-Context Retrieval-Augmented Language ModelsCan we augment the knowledge of LLMs with external information without modifying their architecture?
In this Issue:
💡 ML Concept of the Day: In-Context Retrieval-Augmented Language ModelsRetrieval augmented language models(RALMs) have been the subject of the last few issues of this series. Conceptually, RALMs look to augment the knowledge of an LLM with external information. Most of the common RALM architectures rely on modifying an existing LLM to incorporate the external data. This approach is not always practical and requires significant computation resources... Subscribe to TheSequence to read the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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💡Webinar: To train or not to train your 🅻🅻🅼
Monday, July 3, 2023
While GPT-style models perform admirably initially, developing practical solutions in real-world scenarios remains complex. Is prompt engineering alone sufficient to achieve the desired accuracy? Are
Open Source Scored the First Major M&A of the Generative AI Era
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Next Week in The Sequence: Edge 305: Our generative AI series continues with an overview of in-context, retrieval-augmented LLMs including Google's original paper in the space. We also explore the
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Edge 304: Inside AlphaDev: DeepMind’s Newest Breakthrough Model that Was Able to Discover New Computer Science Alg…
Thursday, June 29, 2023
Built on the foundation created by AlphaZero, the model discovered new and improved existing sorting algorithms.
The Sequence Chat: Daniel J. Mankowitz, DeepMind on Building AlphaDev to Discover New Computer Science Algorithms
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One of the researchers behind DeepMind's groundbreaking model that discovered new sorting algorithms shares his insights about the experience.
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