You Only Use Reactive Attention Slice For Long Context Retrieval

• URL: http://arxiv.org/abs/2409.13695v1
• Date: Tue, 3 Sep 2024 15:30:57 GMT
• Title: You Only Use Reactive Attention Slice For Long Context Retrieval
• Authors: Yun Joon Soh, Hanxian Huang, Yuandong Tian, Jishen Zhao,
• Abstract summary: Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. We propose an attention-based retrieval technique, You Only Use Reactive Attention slice (YOURA) Our technique achieves up to 30% vLLM inference throughput improvement for serving long-context queries.
• Score: 33.712515776334016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, many alternative solutions, such as Retrieval Augmented Generation (RAG), have been used. However, most existing RAG methods adopt embedding-based retrieval that falls short on long contexts. To address such challenges, we propose an attention-based retrieval technique, You Only Use Reactive Attention slice (YOURA). YOURA leverages a novel retrieval heuristic called reaction score to rank the relevance of each sentence in the input context with the query sentence. Intuitively, we measure how the per-token attention score "reacts" to the query and greedily retrieves the most reactive sentences. Internally, YOURA generates a token-indexed vector (called reaction vector) for the whole input context. To map each sentence to the token-indexed vector, we propose an Embedding-Agnostic Sentence Yield (EASY), a best-effort token wiggling algorithm. We evaluate our retrieval technique on three open-source pre-trained LLM models across six LongBench QA datasets. Our technique achieves up to 30% vLLM inference throughput improvement for serving long-context queries with a nearly identical quality score to the simple yet effective truncate-middle approach.

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