Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
Benjamin Bergner Andrii Skliar Amelie Royer Tijmen Blankevoort Yuki Asano Babak Ehteshami Bejnordi
Large language models (LLMs) have become ubiquitous in practice and are
widely used for generation tasks such as translation, summarization and
instruction following. However, their enormous size and reliance on
autoregressive decoding increase deployment costs and complicate their use in
latency-critical applications. In this work, we propose a hybrid approach that
combines language models of different sizes to increase the efficiency of
autoregressive decoding while maintaining high performance. Our method utilizes
a pretrained frozen LLM that encodes all prompt tokens once in parallel, and
uses the resulting representations to condition and guide a small language
model (SLM), which then generates the response more efficiently. We investigate
the combination of encoder-decoder LLMs with both encoder-decoder and
decoder-only SLMs from different model families and only require fine-tuning of
the SLM. Experiments with various benchmarks show substantial speedups of up to
$4\times$, with minor performance penalties of $1-2\%$ for translation and
summarization tasks compared to the LLM.