Computer Science > Computation and Language
[Submitted on 11 Apr 2024 (v1), last revised 17 Oct 2024 (this version, v2)]
Title:LLoCO: Learning Long Contexts Offline
View PDF HTML (experimental)Abstract: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 LLoCO, a novel approach to address this problem by learning contexts offline through context compression and in-domain parameter-efficient finetuning with LoRA. Our method enables an LLM to create a concise representation of the original context and efficiently retrieve relevant information to answer questions accurately. 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 during inference and $11.52\times$ higher throughput during finetuning, substantially reduces the cost of long document question answering. This makes it a promising solution for efficient long context processing. Our code is publicly available on this https URL.
Submission history
From: Xiuyu Li [view email][v1] Thu, 11 Apr 2024 17:57:22 UTC (358 KB)
[v2] Thu, 17 Oct 2024 08:54:37 UTC (264 KB)
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