Nearest Neighbor Speculative Decoding for LLM Generation and Attribution

TL;DR

NEST is a semi-parametric language modeling method that improves generation quality, attribution, and inference speed by integrating real-world text spans through token-level retrieval and speculative decoding.

Contribution

The paper introduces NEST, a novel semi-parametric decoding approach that incorporates arbitrary-length text spans and enhances speed and attribution in LLM generation.

Findings

NEST outperforms kNN-LM in quality and attribution.

NEST achieves 1.8x faster inference on Llama-2-Chat 70B.

NEST performs competitively with in-context retrieval methods.

Abstract

Large language models (LLMs) often hallucinate and lack the ability to provide attribution for their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by refining the output of an LM for a given prompt using its nearest neighbor matches in a non-parametric data store. However, these models often exhibit slow inference speeds and produce non-fluent texts. In this paper, we introduce Nearest Neighbor Speculative Decoding (NEST), a novel semi-parametric language modeling approach that is capable of incorporating real-world text spans of arbitrary length into the LM generations and providing attribution to their sources. NEST performs token-level retrieval at each inference step to compute a semi-parametric mixture distribution and identify promising span continuations in a corpus. It then uses an approximate speculative decoding procedure that accepts a prefix of the retrieved span or generates a new token. NEST significantly enhances the generation quality and attribution rate of the base LM across a variety of knowledge-intensive tasks, surpassing the conventional kNN-LM method and performing competitively with in-context retrieval augmentation. In addition, NEST substantially improves the generation speed, achieving a 1.8x speedup in inference time when applied to Llama-2-Chat 70B. Code will be released at https://github.com/facebookresearch/NEST/tree/main.