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Computer Science > Computation and Language

arXiv:2504.00927 (cs)
[Submitted on 1 Apr 2025]

Title:Multi-Token Attention

Authors:Olga Golovneva, Tianlu Wang, Jason Weston, Sainbayar
Sukhbaatar
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    Abstract:Soft attention is a critical mechanism powering LLMs to
    locate relevant parts within a given context. However, individual
    attention weights are determined by the similarity of only a
    single query and key token vector. This "single token attention"
    bottlenecks the amount of information used in distinguishing a
    relevant part from the rest of the context. To address this
    issue, we propose a new attention method, Multi-Token Attention
    (MTA), which allows LLMs to condition their attention weights on
    multiple query and key vectors simultaneously. This is achieved
    by applying convolution operations over queries, keys and heads,
    allowing nearby queries and keys to affect each other's attention
    weights for more precise attention. As a result, our method can
    locate relevant context using richer, more nuanced information
    that can exceed a single vector's capacity. Through extensive
    evaluations, we demonstrate that MTA achieves enhanced
    performance on a range of popular benchmarks. Notably, it
    outperforms Transformer baseline models on standard language
    modeling tasks, and on tasks that require searching for
    information within long contexts, where our method's ability to
    leverage richer information proves particularly beneficial.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2504.00927 [cs.CL]
          (or arXiv:2504.00927v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2504.00927
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Olga Golovneva [view email]
[v1] Tue, 1 Apr 2025 15:59:32 UTC (2,692 KB)
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