InfiniPot: Infinite Context Processing on Memory-Constrained LLMs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912056519688192 |
|---|---|
| author | Kim, Minsoo Shim, Kyuhong Choi, Jungwook Chang, Simyung |
| author_facet | Kim, Minsoo Shim, Kyuhong Choi, Jungwook Chang, Simyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01518 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InfiniPot: Infinite Context Processing on Memory-Constrained LLMs Kim, Minsoo Shim, Kyuhong Choi, Jungwook Chang, Simyung Computation and Language Machine Learning 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. |
| title | InfiniPot: Infinite Context Processing on Memory-Constrained LLMs |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.01518 |