Nearest Neighbor Speculative Decoding for LLM Generation and Attribution
<|reference_start|>Nearest Neighbor Speculative Decoding for LLM Generation and Attribution: 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.<|reference_end|>
@article{li2024nearest,
    title={Nearest Neighbor Speculative Decoding for LLM Generation and Attribution},
    author={Minghan Li, Xilun Chen, Ari Holtzman, Beidi Chen, Jimmy Lin, Wen-tau
  Yih, Xi Victoria Lin},
    journal={arXiv preprint arXiv:2405.19325},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19325},
    primaryClass={cs.CL}
}