Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass

TL;DR

Superposed Decoding enables the generation of multiple drafts from a single language model inference pass, significantly reducing computational costs while maintaining quality, coherence, and factual accuracy compared to traditional methods.

Contribution

The paper introduces Superposed Decoding, a novel algorithm that produces multiple drafts simultaneously during one inference pass, reducing computational costs without sacrificing quality.

Findings

At least 2.44× faster for k≥3 drafts.

Drafts are as coherent and factual as existing sampling methods.

User evaluations favor Superposed Decoding over Nucleus Sampling.

Abstract

Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggestions. Under the hood, language models support this by running an autoregressive inference pass to provide a draft. Consequently, providing $k$ drafts to the user requires running an expensive language model $k$ times. To alleviate the computation cost of running $k$ inference passes, we propose Superposed Decoding, a new decoding algorithm that generates $k$ drafts at the computation cost of one autoregressive inference pass. We achieve this by feeding a superposition of the most recent token embeddings from the $k$ drafts as input to the next decoding step of the language model. At every inference step we combine the $k$ drafts with the top-$k$ tokens to get $k^2$ new drafts and cache the $k$ most likely options, using an n-gram interpolation with minimal compute overhead to filter out incoherent generations. Our experiments show that $k$ drafts from Superposed Decoding are at least as coherent and factual as Nucleus Sampling and Greedy Decoding respectively, while being at least $2.44\times$ faster for $k\ge3$. In a compute-normalized setting, user evaluations demonstrably favor text generated by Superposed Decoding over Nucleus Sampling. Superposed Decoding can also be combined with other decoding strategies, resulting in universal coverage gains when scaling inference time compute. Code and more examples open-sourced at https://github.com/RAIVNLab/SuperposedDecoding.