Adaptive Head Budgeting for Efficient Multi-Head Attention

2026-04-24

AI Summary

This work addresses the computational redundancy in standard multi-head attention mechanisms, which uniformly activate all attention heads regardless of task requirements or input complexity. To overcome this limitation, the authors propose BudgetFormer, the first framework that enables input-dependent dynamic head budget allocation and selection. Specifically, it employs adaptive multi-head attention to dynamically determine the optimal number of heads for each input and selects the most informative ones. An exploration-exploitation balanced training strategy is further introduced to optimize resource allocation. Extensive experiments on multiple text classification benchmarks demonstrate that BudgetFormer significantly reduces FLOPs and memory consumption while achieving performance comparable to or better than full-head attention models.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Computer Vision: Large Vision Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Transformers have become the dominant architecture across a wide range of domains, largely due to the effectiveness of multi-head attention in capturing diverse representation subspaces. However, standard multi-head attention activates all heads uniformly for every input, regardless of task requirements or input complexity. In many scenarios, particularly for coarse-grained tasks such as text classification, the relevant information is often global and does not require the full diversity of attention heads. As a consequence, using a fixed number of heads can introduce unnecessary computational cost or lead to suboptimal performance when the allocation does not match the input. To address this limitation, we introduce BudgetFormer, a Transformer architecture equipped with an adaptive multi-head attention mechanism that dynamically allocates computational resources. Our approach learns, for each input, both a head budget corresponding to the number of attention heads required, and a relevance distribution that selects the most informative heads. We also propose a training strategy based on an exploration and exploitation trade-off, allowing the model to discover effective head configurations before converging to efficient usage patterns. Experiments on text classification tasks of varying complexity show that our method reduces inference cost in terms of FLOPs and memory, while also achieving performance that can surpass standard full multi-head attention. These results highlight the potential of adaptive head allocation as a principled approach to improving both efficiency and effectiveness in Transformer models.

Problem

• multi-head attention
• computational efficiency
• adaptive allocation
• Transformer models
• head budgeting

Innovation

• adaptive head budgeting
• multi-head attention
• efficient Transformers
• dynamic resource allocation
• text classification