SpeLLM: Character-Level Multi-Head Decoding

• URL: http://arxiv.org/abs/2507.16323v1
• Date: Tue, 22 Jul 2025 08:07:06 GMT
• Title: SpeLLM: Character-Level Multi-Head Decoding
• Authors: Amit Ben-Artzy, Roy Schwartz,
• Abstract summary: SpeLLM is a method that decouples input and output vocabularies by predicting character-level strings through multiple output heads.<n>In SpeLLM, each of the $k$ linear heads predicts a single character simultaneously, enabling the model to represent a much larger output space.<n>Our experiments with four pre-trained LLMs show their SpeLLM variants achieve competitive performance on downstream tasks while reducing runtime by 5.1%.
• Score: 6.474939955469084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scaling LLM vocabulary is often used to reduce input sequence length and alleviate attention's quadratic cost. Yet, current LLM architectures impose a critical bottleneck to this procedure: the output projection layer scales linearly with vocabulary size, rendering substantial expansion impractical. We propose SpeLLM, a method that decouples input and output vocabularies by predicting character-level strings through multiple output heads. In SpeLLM, each of the $k$ linear heads predicts a single character simultaneously, enabling the model to represent a much larger output space using smaller, independent linear heads. We present a self-distillation approach for converting a standard LLM to a SpeLLM. Our experiments with four pre-trained LLMs show their SpeLLM variants achieve competitive performance on downstream tasks while reducing runtime by 5.1% on average across models. Our approach provides a potential avenue for reducing LLM costs, while increasing support for underrepresented languages and domains.

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