Consistency LLMs

proposes efficient parallel decoders that reduce inference latency by decoding n-token sequence per inference step; the inspiration for this work comes from the human's ability to form complete sentences before articulating word by word; this process can be mimicked and learned through fine-tuning pre-trained LLMs to perform parallel decoding; it is trained to perform parallel decoding by mapping randomly initialized n-token sequences to the same result yielded by autoregressive (AR) decoding in as few steps as possible; a consistency loss helps with multiple-token prediction and a standard AR loss prevents deviation from the target LLM and ensures generation quality. Shows 2.4x to 3.4x improvements in generation speed while preserving the generation quality.