LLoCO: Learning Long Contexts Offline

Sijun Tan, Xiuyu Li, Shishir Patil, wuziyang, Tianjun Zhang, Kurt Keutzer, Joseph E. Gonzalez, Raluca Ada Popa

LLoCO: Learning Long Contexts Offline: 22 upvotes on Hugging Face Daily Papers, #12 of 15 papers on 2024-04-12. Day-by-day upvote history.

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 30times fewer tokens during inference. LLoCO achieves up to 7.62times 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.

Paper page on Hugging Face · arXiv

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