How does the retrieval efficiency of Llama-3-8B-128K vary across context lengths 32K, 64K, and 128K on the MuS

Abstract

Abstract: Processing long contexts presents a significant challenge for large language models (LLMs). While recent advancements allow LLMs to handle much longer contexts than before (e.g., 32K or 128K tokens), it is computationally expensive and can still be insufficient for many applications. Retrieval-Augmented Generation (RAG) is considered a promising strategy to address this problem. However, conventional RAG methods face inherent limitations because of two underlying requirements: 1) explicitly stated queries, and 2) well-structured knowledge. These conditions, however, do not hold in general long

Research Question

How does the retrieval efficiency of Llama-3-8B-128K vary across context lengths 32K, 64K, and 128K on the MuSiQue benchmark when using the Tree of Reviews framework compared to chain-based retrieval?

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