InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917975563436032 |
|---|---|
| author | Cao, Bowen Cai, Deng Lam, Wai |
| author_facet | Cao, Bowen Cai, Deng Lam, Wai |
| contents | In-context learning (ICL) is critical for large language models (LLMs), but its effectiveness is constrained by finite context windows, particularly in ultra-long contexts. To overcome this, we introduce InfiniteICL, a framework that parallels context and parameters in LLMs with short- and long-term memory in human cognitive systems, focusing on transforming temporary context knowledge into permanent parameter updates. This approach significantly reduces memory usage, maintains robust performance across varying input lengths, and theoretically enables infinite context integration through the principles of context knowledge elicitation, selection, and consolidation. Evaluations demonstrate that our method reduces context length by 90% while achieving 103% average performance of full-context prompting across fact recall, grounded reasoning, and skill acquisition tasks. When conducting sequential multi-turn transformations on complex, real-world contexts (with length up to 2M tokens), our approach surpasses full-context prompting while using only 0.4% of the original contexts. These findings highlight InfiniteICL's potential to enhance the scalability and efficiency of LLMs by breaking the limitations of conventional context window sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation Cao, Bowen Cai, Deng Lam, Wai Computation and Language Artificial Intelligence In-context learning (ICL) is critical for large language models (LLMs), but its effectiveness is constrained by finite context windows, particularly in ultra-long contexts. To overcome this, we introduce InfiniteICL, a framework that parallels context and parameters in LLMs with short- and long-term memory in human cognitive systems, focusing on transforming temporary context knowledge into permanent parameter updates. This approach significantly reduces memory usage, maintains robust performance across varying input lengths, and theoretically enables infinite context integration through the principles of context knowledge elicitation, selection, and consolidation. Evaluations demonstrate that our method reduces context length by 90% while achieving 103% average performance of full-context prompting across fact recall, grounded reasoning, and skill acquisition tasks. When conducting sequential multi-turn transformations on complex, real-world contexts (with length up to 2M tokens), our approach surpasses full-context prompting while using only 0.4% of the original contexts. These findings highlight InfiniteICL's potential to enhance the scalability and efficiency of LLMs by breaking the limitations of conventional context window sizes. |
| title | InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.01707 |