Skeleton of Thought: LLMs Can Do Parallel Decoding

Deep Papers is a podcast series featuring deep dives on today’s seminal AI papers and research. Each episode profiles the people and techniques behind cutting-edge breakthroughs in machine learning. In this paper reading, we explore the paper ‘Skeleton-of-Thought’ (SoT) approach, aimed at reducing large language model latency while enhancing answer quality.
This episode is led by Aparna Dhinakaran ( Chief Product Officer, Arize AI) and Sally-Ann Delucia (ML Solutions Engineer, Arize AI), with two of the paper authors: Xuefei Ning, Postdoctoral Researcher at Tsinghua University and Zinan Lin, Senior Researcher, Microsoft Research.
SoT’s innovative methodology guides LLMs to construct answer skeletons before parallel content elaboration, achieving impressive speed-ups of up to 2.39x across 11 models. Don’t miss the opportunity to delve into this human-inspired optimization strategy and its profound implications for efficient and high-quality language generation.
Full transcript and more here: https://arize.com/blog/skeleton-of-thought-llms-can-do-parallel-decoding-paper-reading/

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