LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Currently, large language models (LLMs) train on short text segments due to the computational overhead quadratic in the input lengths of their Transformer architectures. |
| Approach: | They propose a method that allows LLMs pre-trained with 2K or 4K-long segments to generalize to up to 200M length inputs while retaining perplexity. |
| Outcome: | The proposed method achieves 2.7 decoding speed up and 7.5 memory saving over the original model. |
Similar Papers
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models (2025.acl-long)
Copied to clipboard
Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, WangYan WangYan, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi
| Challenge: | Large language models face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. |
| Approach: | They propose a training strategy for extending the context window of LLMs including impactful token analysis, position index transformation, and training optimization strategies. |
| Outcome: | Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size. |
E2-LLM: Efficient and Extreme Length Extension of Large Language Models (2024.findings-acl)
Copied to clipboard
Jiaheng Liu, ZhiqiBai ZhiqiBai, Yuanxing Zhang, Chenchen Zhang, YuangZh YuangZh, Ge Zhang, JiakaiWang JiakaiWang, Haoran Que, Yukang Chen, Wenbo Su, Tiezheng Ge, Jie Fu, Wenhu Chen, Bo Zheng
| Challenge: | Existing techniques for extending context capabilities in LLMs require additional training procedures and access to datasets with long context (e.g., sequences of 32K tokens). |
| Approach: | They propose a solution to extend context capabilities in Large Language Models by training a single process over a sequence of 4K tokens. |
| Outcome: | The proposed solution significantly reduces the cost of continual-pretraining or fine-tuning over short sequences and improves robustness to diverse relative positions. |
SirLLM: Streaming Infinite Retentive LLM (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming increasingly prevalent in various domains, requiring a one-off input of overly long texts to maintain a degree of memory. |
| Approach: | They propose a Streaming Infinite Retentive LLM which allows LLMs to maintain longer memory during infinite-length dialogues without fine-tuning. |
| Outcome: | The proposed model can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. |
LLMs Are Zero-Shot Context-Aware Simultaneous Translators (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing SiMT systems operate on a sentence level, disregarding the context established by previous sentences or the broader context implied by previous words. |
| Approach: | They show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
| Outcome: | The proposed models perform on par with or better than state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
Copied to clipboard
Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Long-context capabilities are essential for document and video understanding, in-contact learning, and inference-time scaling. |
| Approach: | They propose an efficient training recipe for building ultra-long context LLMs from aligned instruct model, pushing the boundaries of context lengths from 128K to 1M, 2M, and 4M tokens. |
| Outcome: | The proposed model extends the context window while maintaining short context capabilities while maintaining the performance of the existing model. |
InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation (2025.findings-acl)
Copied to clipboard
| Challenge: | InfiniteICL is a framework that parallels context and parameters in large language models with short- and long-term memory in human cognitive systems. |
| Approach: | They propose a framework that parallels context and parameters in large language models with short- and long-term memory in human cognitive systems and enables infinite context integration. |
| Outcome: | The proposed framework reduces context length by 90% while achieving 103% average performance of full-context prompting across fact recall, grounded reasoning, and skill acquisition tasks. |
Long-Form Speech Translation through Segmentation with Finite-State Decoding Constraints on Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. |
| Approach: | They propose a large language model to split long ASR transcripts into segments that can be independently translated to maximize translation quality. |
| Outcome: | The proposed model improves the average BLEU by 2.9 points for English–German, English–Spanish, and English–Arabic TED talk translation in 9 sets. |
Zero-Shot Strategies for Length-Controllable Summarization (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large language models struggle with precise length control, particularly in zero-shot settings. |
| Approach: | They propose to use length approximation, target adjustment, sample filtering and automated revisions to improve LLMs' length control capabilities. |
| Outcome: | The proposed methods improve length control in large language models while maintaining or enhancing summary quality without the need for model fine-tuning or architectural changes. |
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
| Approach: | They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English. |
| Outcome: | The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English. |