MemLong: Memory-Augmented Retrieval for Long Text Modeling

• URL: http://arxiv.org/abs/2408.16967v1
• Date: Fri, 30 Aug 2024 02:01:56 GMT
• Title: MemLong: Memory-Augmented Retrieval for Long Text Modeling
• Authors: Weijie Liu, Zecheng Tang, Juntao Li, Kehai Chen, Min Zhang,
• Abstract summary: This work introduces MemLong: Memory-Augmented Retrieval for Long Text Generation. MemLong combines a non-differentiable ret-mem'' module with a partially trainable decoder-only language model. Comprehensive evaluations on multiple long-context language modeling benchmarks demonstrate that MemLong consistently outperforms other state-of-the-art LLMs.
• Score: 37.49036666949963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs due to the quadratic time and space complexity of attention mechanisms and the growing memory consumption of the key-value cache during generation. This work introduces MemLong: Memory-Augmented Retrieval for Long Text Generation, a method designed to enhance the capabilities of long-context language modeling by utilizing an external retriever for historical information retrieval. MemLong combines a non-differentiable ``ret-mem'' module with a partially trainable decoder-only language model and introduces a fine-grained, controllable retrieval attention mechanism that leverages semantic-level relevant chunks. Comprehensive evaluations on multiple long-context language modeling benchmarks demonstrate that MemLong consistently outperforms other state-of-the-art LLMs. More importantly, MemLong can extend the context length on a single 3090 GPU from 4k up to 80k. Our code is available at https://github.com/Bui1dMySea/MemLong

Related papers

• Needle in the Haystack for Memory Based Large Language Models [31.885539843977472]
  Current large language models (LLMs) often perform poorly on simple fact retrieval tasks. We investigate if coupling a dynamically adaptable external memory to a LLM can alleviate this problem. We demonstrate that the external memory of Larimar, which allows fast write and read of an episode of text samples, can be used at test time to handle contexts much longer than those seen during training.
  arXiv  Detail & Related papers  (2024-07-01T16:32:16Z)
• Long Context Transfer from Language to Vision [74.78422371545716]
  Video sequences offer valuable temporal information, but existing large multimodal models (LMMs) fall short in understanding extremely long videos. In this paper, we approach this problem from the perspective of the language model. By simply extrapolating the context length of the language backbone, we enable LMMs to comprehend orders of magnitude more visual tokens without any video training.
  arXiv  Detail & Related papers  (2024-06-24T17:58:06Z)
• HMT: Hierarchical Memory Transformer for Long Context Language Processing [35.730941605490194]
  Hierarchical Memory Transformer (HMT) is a novel framework that enables and improves models' long-context processing ability. We show that HMT steadily improves the long-context processing ability of context-constrained and long-context models.
  arXiv  Detail & Related papers  (2024-05-09T19:32:49Z)
• LongAlign: A Recipe for Long Context Alignment of Large Language Models [61.85923382850057]
  LongAlign is a recipe of the instruction data, training, and evaluation for long context alignment. We construct a long instruction-following dataset using Self-Instruct. We adopt the packing and sorted strategies to speed up supervised fine-tuning on data with varied length distributions.
  arXiv  Detail & Related papers  (2024-01-31T18:29:39Z)
• LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding [58.20031627237889]
  LongBench is the first bilingual, multi-task benchmark for long context understanding. It comprises 21 datasets across 6 task categories in both English and Chinese, with an average length of 6,711 words (English) and 13,386 characters (Chinese)
  arXiv  Detail & Related papers  (2023-08-28T11:53:40Z)
• Augmenting Language Models with Long-Term Memory [142.04940250657637]
  Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit. We propose a framework, Language Models Augmented with Long-Term Memory (LongMem), which enables LLMs to memorize long history.
  arXiv  Detail & Related papers  (2023-06-12T15:13:39Z)
• LaMemo: Language Modeling with Look-Ahead Memory [50.6248714811912]
  We propose Look-Ahead Memory (LaMemo) that enhances the recurrence memory by incrementally attending to the right-side tokens. LaMemo embraces bi-directional attention and segment recurrence with an additional overhead only linearly proportional to the memory length. Experiments on widely used language modeling benchmarks demonstrate its superiority over the baselines equipped with different types of memory.
  arXiv  Detail & Related papers  (2022-04-15T06:11:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.