InfiniPot: Infinite Context Processing on Memory-Constrained LLMs
Minsoo Kim, Kyuhong Shim, Jungwook Choi, Simyung Chang
2024-10-03
Summary
This paper discusses InfiniPot, a new framework designed to help large language models (LLMs) efficiently process long inputs while using limited memory, making them more effective for real-world applications.
What's the problem?
Large language models are great at understanding and generating text, but they struggle with long input sequences, especially when they have limited memory, like on mobile devices. This can lead to difficulties in processing information and producing accurate results when the input is too lengthy for the model to handle effectively.
What's the solution?
To tackle this issue, the authors introduced InfiniPot, which allows LLMs to manage long sequences of data without needing extra memory. It does this by using a method called Continual Context Distillation (CCD) that helps the model focus on the most important information from the input. Instead of trying to remember everything, InfiniPot compresses and retains only the essential details, allowing the model to work efficiently even with long texts. This approach means that LLMs can now process longer contexts effectively without requiring additional training.
Why it matters?
This research is important because it enhances the capabilities of language models, making them more practical for everyday use on devices with limited resources. By improving how these models handle long inputs, InfiniPot can lead to better performance in applications like chatbots, virtual assistants, and other AI tools that need to understand complex or lengthy information.
Abstract
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.