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How well can AI remember and use information in long conversations?

This episode explores the groundbreaking LOCOMO dataset, a unique resource designed to evaluate long-term conversational memory in Large Language Models (LLMs).

We delve into the challenges of current AI in maintaining coherent, empathetic conversations over multiple sessions. Discover how the LOCOMO dataset, generated through a human-machine pipeline with unique personas, temporal event graphs, and multimodal dialogue capabilities, is pushing the boundaries of conversational AI.

We discuss key findings from experiments using base models, long-context LLMs, and Retrieval Augmented Generation (RAG) techniques, revealing limitations and promising approaches for improving long-term memory. We'll also examine the ethical considerations of creating realistic conversational agents that can remember our past interactions.

Learn about the importance of structured information like observations about speakers and retrieval based methods, in order to create truly conversational AI.

LOCOMO: Unlocking Long-Term Memory in Conversational AI

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