Lloco: Learning Long Contexts Offline

Tan Sijun, Li Xiuyu, Patil Shishir, Wu Ziyang, Zhang Tianjun, Keutzer Kurt, Gonzalez Joseph E., Popa Raluca Ada. Arxiv 2024

[Paper] [Code]    
Applications Attention Mechanism Fine Tuning Has Code In Context Learning Model Architecture Prompting Transformer

Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. We propose a novel approach to address this problem by learning contexts offline through context compression and in-domain parameter-efficient finetuning. Our method enables an LLM to create a concise representation of the original context and efficiently retrieve relevant information to answer questions accurately. We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA. Our approach extends the effective context window of a 4k token LLaMA2-7B model to handle up to 128k tokens. We evaluate our approach on several long-context question-answering datasets, demonstrating that LLoCO significantly outperforms in-context learning while using \(30\times\) fewer tokens during inference. LLoCO achieves up to \(7.62\times\) speed-up and substantially reduces the cost of long document question answering, making it a promising solution for efficient long context processing. Our code is publicly available at https://github.com/jeffreysijuntan/lloco.

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