Inference with Reference: Lossless Acceleration of Large Language Models

• URL: http://arxiv.org/abs/2304.04487v1
• Date: Mon, 10 Apr 2023 09:55:14 GMT
• Title: Inference with Reference: Lossless Acceleration of Large Language Models
• Authors: Nan Yang, Tao Ge, Liang Wang, Binxing Jiao, Daxin Jiang, Linjun Yang, Rangan Majumder, Furu Wei
• Abstract summary: 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.
• Score: 97.04200102556551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA 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 (e.g., retrieved documents). LLMA first selects a text span from the reference and copies its tokens to the decoder and then efficiently checks the tokens' appropriateness as the decoding result in parallel within one decoding step. The improved computational parallelism allows LLMA to achieve over 2x speed-up for LLMs with identical generation results as greedy decoding in many practical generation scenarios where significant overlap between in-context reference and outputs exists (e.g., search engines and multi-turn conversations).

Related papers

• Text-like Encoding of Collaborative Information in Large Language Models for Recommendation [58.87865271693269]
  We introduce BinLLM, a novel method to seamlessly integrate collaborative information with Large Language Models for Recommendation (LLMRec) BinLLM converts collaborative embeddings from external models into binary sequences. BinLLM provides options to compress the binary sequence using dot-decimal notation to avoid excessively long lengths.
  arXiv  Detail & Related papers  (2024-06-05T12:45:25Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding [15.723047976314751]
  Large language models (LLMs) have become ubiquitous in practice and are widely used for generation tasks such as translation, summarization and instruction following. We propose a hybrid approach that combines language models of different sizes to increase the efficiency of autoregressive decoding.
  arXiv  Detail & Related papers  (2024-02-26T18:59:28Z)
• Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding [11.832919020149891]
  This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose textbfSmart textbfParallel textbfAuto-textbfCorrect dtextbfEcoding (SPACE)
  arXiv  Detail & Related papers  (2024-02-19T03:39:10Z)
• Making Large Language Models A Better Foundation For Dense Retrieval [19.38740248464456]
  Dense retrieval needs to learn discriminative text embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs), given LLMs' strong capability on semantic understanding. We propose LLaRA (LLM adapted for dense RetrievAl), which works as a post-hoc adaptation of dense retrieval application.
  arXiv  Detail & Related papers  (2023-12-24T15:10:35Z)
• Speech Translation with Large Language Models: An Industrial Practice [64.5419534101104]
  We introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained large language model (LLM) By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST.
  arXiv  Detail & Related papers  (2023-12-21T05:32:49Z)
• LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
  Recent large language models (LLM) are leveraging human feedback to improve their generation quality. We propose LLMRefine, an inference time optimization method to refine LLM's output. We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization. LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
  arXiv  Detail & Related papers  (2023-11-15T19:52:11Z)
• Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster [61.83949316226113]
  FastCoT is a model-agnostic framework based on parallel decoding. We show that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach.
  arXiv  Detail & Related papers  (2023-11-14T15:56:18Z)
• Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition [23.172469312225694]
  We propose to utilize an instruction-tuned large language model (LLM) for guiding the text generation process in automatic speech recognition (ASR) The proposed model is built on the joint CTC and attention architecture, with the LLM serving as a front-end feature extractor for the decoder. Experimental results show that the proposed LLM-guided model achieves a relative gain of approximately 13% in word error rates across major benchmarks.
  arXiv  Detail & Related papers  (2023-09-19T11:10:50Z)
• 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)

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.