InfiniPot: Infinite Context Processing on Memory-Constrained LLMs

• DOI: 10.18653/v1/2024.emnlp-main.897
• Publication Date: 2024-11-27T22:28:12Z
• Authors: Minsoo Kim; Kyuhong Shim; Jungwook Choi; Simyung Chang
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Computation and Language (cs.CL); Machine Learning (cs.LG)

Handling long input contexts remains a significant challenge for Large Language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work aims to address this limitation by introducing InfiniPot, a novel KV cache control framework designed to enable pre-trained LLMs to manage extensive sequences within fixed memory constraints efficiently, without requiring additional training. InfiniPot leverages Continual Context Distillation (CCD), an iterative process that compresses and retains essential information through novel importance metrics, effectively maintaining critical data even without access to future context. Our comprehensive evaluations indicate that InfiniPot significantly outperforms models trained for long contexts in various NLP tasks, establishing its efficacy and versatility. This work represents a substantial advancement toward making LLMs applicable to a broader range of real-world scenarios.<br/>EMNLP 2024 Main<br/>