Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

• URL: http://arxiv.org/abs/2307.15337v3
• Date: Sat, 2 Mar 2024 02:45:33 GMT
• Title: Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
• Authors: Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang
• Abstract summary: This work aims at decreasing the end-to-end generation latency of large language models (LLMs) We propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. SoT is an initial attempt at data-centric optimization for inference efficiency, and showcases the potential of eliciting high-quality answers by explicitly planning the answer structure in language.
• Score: 23.65270067167911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-ups across 12 LLMs, but it can also potentially improve the answer quality on several question categories. SoT is an initial attempt at data-centric optimization for inference efficiency, and showcases the potential of eliciting high-quality answers by explicitly planning the answer structure in language.

Related papers

• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• LinkGPT: Teaching Large Language Models To Predict Missing Links [23.57145845001286]
  Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, particularly on Text-Attributed Graphs (TAGs)
  arXiv  Detail & Related papers  (2024-06-07T04:54:36Z)
• Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models [9.121458241884444]
  Speculative execution is introduced to LLM decoding in a textitdraft-then-verify style. As the costly inference is parallelized, decoding speed can be significantly boosted. We present the first survey paper that reviews and unifies literature of speculative execution in LLMs.
  arXiv  Detail & Related papers  (2024-04-23T10:25:45Z)
• Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves [57.974103113675795]
  We present a method named Rephrase and Respond' (RaR) which allows Large Language Models to rephrase and expand questions posed by humans. RaR serves as a simple yet effective prompting method for improving performance. We show that RaR is complementary to the popular Chain-of-Thought (CoT) methods, both theoretically and empirically.
  arXiv  Detail & Related papers  (2023-11-07T18:43:34Z)
• LLMCad: Fast and Scalable On-device Large Language Model Inference [11.103824752113148]
  Generative tasks, such as text generation and question answering, hold a crucial position in the realm of mobile applications. Currently, the execution of these generative tasks heavily depends on Large Language Models (LLMs) We introduce LLMCad, an on-device inference engine specifically designed for efficient generative Natural Language Processing (NLP) tasks.
  arXiv  Detail & Related papers  (2023-09-08T10:44:19Z)
• LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models [83.98062659664785]
  Large language models (LLMs) typically train on short text segments (e.g., 4K tokens) due to the quadratic complexity of their Transformer architectures. This work identifies three major factors contributing to this length generalization failure. We propose LM-Infinite, a simple and effective method for enhancing LLMs' capabilities of handling long contexts.
  arXiv  Detail & Related papers  (2023-08-30T16:47:51Z)
• Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline [22.08897444328099]
  Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
  arXiv  Detail & Related papers  (2023-05-22T15:36:06Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)
• SatLM: Satisfiability-Aided Language Models Using Declarative Prompting [68.40726892904286]
  We propose a new satisfiability-aided language modeling (SatLM) approach for improving the reasoning capabilities of large language models (LLMs) We use an LLM to generate a declarative task specification rather than an imperative program and leverage an off-the-shelf automated theorem prover to derive the final answer. We evaluate SATLM on 8 different datasets and show that it consistently outperforms program-aided LMs in the imperative paradigm.
  arXiv  Detail & Related papers  (2023-05-16T17:55:51Z)
• Inference with Reference: Lossless Acceleration of Large Language Models [97.04200102556551]
  LLMA is an accelerator to speed up Large Language Model (LLM) inference with references. It is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real world scenarios.
  arXiv  Detail & Related papers  (2023-04-10T09:55:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.