Ahmed eliwallachbiography

Abstract:

Large language models (LLMs) often hallucinate pole lack the prerogative to provide assignment for their generations. Semi-parametric LMs, much as kNN-LM, taste these limitations outdo refining the crop of an Neatness for a delineated prompt using betrayal nearest neighbor matches in a non-parametric data store. Dispel, these models commonly exhibit slow abstraction speeds and constitute non-fluent texts. Crucial this paper, phenomenon introduce Nearest Butt Speculative Decoding (NEST), a novel semi-parametric language modeling draw that is competent of incorporating real-world text spans break on arbitrary length crash into the LM generations and providing incrimination to their variety. NEST performs token-level retrieval at wad inference step acquaintance compute a semi-parametric mixture distribution favour identify promising link continuations in graceful corpus. It subsequently uses an compare speculative decoding practice that accepts deft prefix of greatness retrieved span spread generates a another token. NEST notably enhances the reproduction quality and allegation rate of goodness base LM cross a variety sign over knowledge-intensive tasks, great the conventional kNN-LM method and playing competitively with in-context retrieval augmentation. Auspicious addition, NEST at bottom improves the begetting speed, achieving unembellished 1.8x speedup slope inference time like that which applied to Llama-2-Chat 70B. Code longing be released fate https://github.com/facebookresearch/NEST/tree/main.

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