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Computer Science > Computation and Language

arXiv:2405.06067 (cs)
[Submitted on 9 May 2024 (v1), last revised 14 May 2024 (this
version, v2)]

Title:HMT: Hierarchical Memory Transformer for Long Context Language
Processing

Authors:Zifan He, Zongyue Qin, Neha Prakriya, Yizhou Sun, Jason Cong
View a PDF of the paper titled HMT: Hierarchical Memory Transformer
for Long Context Language Processing, by Zifan He and 4 other authors
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    Abstract:Transformer-based large language models (LLM) have been
    widely used in language processing applications. However, most of
    them restrict the context window that permits the model to attend
    to every token in the inputs. Previous works in recurrent models
    can memorize past tokens to enable unlimited context and maintain
    effectiveness. However, they have "flat" memory architectures,
    which have limitations in selecting and filtering information.
    Since humans are good at learning and self-adjustment, we
    speculate that imitating brain memory hierarchy is beneficial for
    model memorization. We propose the Hierarchical Memory
    Transformer (HMT), a novel framework that enables and improves
    models' long-context processing ability by imitating human
    memorization behavior. Leveraging memory-augmented segment-level
    recurrence, we organize the memory hierarchy by preserving tokens
    from early input token segments, passing memory embeddings along
    the sequence, and recalling relevant information from history.
    Evaluating general language modeling (Wikitext-103, PG-19) and
    question-answering tasks (PubMedQA), we show that HMT steadily
    improves the long-context processing ability of
    context-constrained and long-context models. With an additional
    0.5% - 2% of parameters, HMT can easily plug in and augment
    future LLMs to handle long context effectively. Our code is
    open-sourced on Github: this https URL.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2405.06067 [cs.CL]
          (or arXiv:2405.06067v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2405.06067
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Zifan He [view email]
[v1] Thu, 9 May 2024 19:32:49 UTC (1,251 KB)
[v2] Tue, 14 May 2024 06:09:52 UTC (1,251 KB)
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