在开放性文本生成任务中,通常使用截断抽样(truncated sampling),因为模型下的最可能字符串往往很短而缺乏趣味性。而在解决推理问题时,通常会优先选择贪婪解码(greedy decoding),以避免采样错误。然而,在推理任务中使用这种贪婪解码增加了在开放性生成中出现推理错误的可能性。 本文引入CD在解决推理问题时优于贪婪解码,提...
Large language modelsAutonomous vehicleMoral dilemmasAI ethicsChoice experimentsOur study investigated the ethical dilemma of prioritizing saving pedestrians or passengers.SP scenarios with group size, age, gender, fatality risk, and pedestrian behavior were used to analyze LLM responses.Binary logic model ...
ToT:Tree of Thoughts: Deliberate Problem Solving with Large Language Models.2023 RAP:Reasoning with Language Model is Planning with World Model.2023 核心idea:结合树搜索算法,指导多步推理,增强llm推理能力 (1)ToT(Tree-of-Thought):模仿人类慢思考,利用BFS/DFS提升LLM的规划能力。 (2)RAP(Reasoning via ...
AdaEDL:EarlyDraftStoppingforSpeculativeDecodingofLargeLanguageModelsviaanEntropy-basedLowerBoundonTokenAcceptanceProbabilitySudhanshuAgrawalWonseokJeonMinguLeeQualcommAIResearch1sudhagrawjeonmingul@qti.qualcomm.comAbstractSpeculativedecoding[1]isapowerfu
Code for the ICLR 2024 paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models" Paper:https://arxiv.org/abs/2309.03883 Authors:Yung-Sung Chuang†,Yujia Xie‡,Hongyin Luo†,Yoon Kim†,James Glass†,Pengcheng He‡ ...
This repository implements speculative sampling for large language model (LLM) decoding. It utilizes two models during the decoding process: a target model and an approximation model. The approximation model is a smaller model, while the target model is a larger one. The approximation model generate...
Speculative decoding is a powerful technique that attempts to circumvent the autoregressive constraint of modern Large Language Models (LLMs). The aim of speculative decoding techniques is to improve the average inference time of a large, target model without sacrificing its accuracy, by using a more...
However, the deployment of AI and Large Language Models in government sectors brings with it the imperative of adhering to stringent standards of data privacy, ethics, and quality. In the context of government operations, where data sensitivity and accuracy are paramount, it’s essential to ensure...
Inference with Multimodal Large Language Models (MLLMs) is slow due to their large-language-model backbone which suffers from memory bandwidth bottleneck and generates tokens auto-regressively. In this paper, we explore the application of speculative decoding to enhance the inference efficiency of ML...
Speculative decoding is a pivotal technique to accelerate the inference of large language models (LLMs) by employing a smaller draft model to predict the target model's outputs. However, its efficacy can be limited due to the low predictive accuracy of the draft model, particularly when faced ...