LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs

• URL: http://arxiv.org/abs/2502.06139v1
• Date: Mon, 10 Feb 2025 04:02:18 GMT
• Title: LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs
• Authors: Sumin An, Junyoung Sung, Wonpyo Park, Chanjun Park, Paul Hongsuck Seo,
• Abstract summary: We introduce Long-form Context Injection with Recurrent Compression (LCIRC), a method that enables the efficient processing long-form sequences beyond the model's length limit. We also introduce query dependent context modeling, which selectively compresses query-relevant information, ensuring that the model retains the most pertinent content.
• Score: 10.84210988032097
• License:
• Abstract: While large language models (LLMs) excel in generating coherent and contextually rich outputs, their capacity to efficiently handle long-form contexts is limited by fixed-length position embeddings. Additionally, the computational cost of processing long sequences increases quadratically, making it challenging to extend context length. To address these challenges, we propose Long-form Context Injection with Recurrent Compression (LCIRC), a method that enables the efficient processing long-form sequences beyond the model's length limit through recurrent compression without retraining the entire model. We further introduce query dependent context modeling, which selectively compresses query-relevant information, ensuring that the model retains the most pertinent content. Our empirical results demonstrate that Query Dependent LCIRC (QD-LCIRC) significantly improves LLM's ability to manage extended contexts, making it well-suited for tasks that require both comprehensive context understanding and query relevance.

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