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Explore the full engineering blog here: https://research.google/blog/chain-of-agents-large-language-models-collaborating-on-long-context-tasks/

Welcome to Blog Bytes! Today, we're diving into the fascinating world of large language models. While LLMs have wowed us with their abilities in reasoning, knowledge retrieval, and text generation, they often stumble when handling long inputs—making tasks like extended summarization and detailed question answering a real challenge.

At NeurIPS 2024, a breakthrough came with the introduction of the Chain-of-Agents framework. This innovative approach leverages multiple agents working together through natural language to overcome context length limitations, significantly boosting performance on long-context tasks. In our discussion, we'll explore how CoA outperforms traditional methods, achieving up to a 10% improvement over existing baselines.

Stay tuned as we unpack the potential of Chain-of-Agents and what it means for the future of LLMs!

Chain of Agents: Large language models collaborating on long-context tasks (Google Research)

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