但Decoder-Only 架构在处理过程中由于不需要同时考虑编码和解码两个环节对长序列的处理，其内部的注意力...
先把目光放放到模型参数动辄100B之前的时代,看看小一点的模型参数量下、两个架构各有什么优势——Google Brain 和 HuggingFace联合发表的 What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? 曾经在5B的参数量级下对比了两者性能。 论文最主要的一个结论是:decoder-...
原因1：过往研究证明decoder-only泛化化性能更好Google有两篇著名的发表于ICML’22的论文，一个是《Examining Scaling and Transfer of Language Model Architectures for Machine Translation》，另一个是《What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?》，两篇论文...
Apart from the various interesting features of this model, one feature that catches the attention is its decoder-only architecture. In fact, not just PaLM, some of the most popular and widely used language models are decoder-only.
A natural question to ask is: which architecture is the best choice. According to previous studies, when the amount of training dataset is sufficient, using the full Transformer is the priority choice for NLG tasks. However, for the insufficient training dataset setting, we find this is not ...
Efficient encoder-decoder architecture for small language models (≤1B parameters) with cross-architecture knowledge distillation and vision-language capabilities encoder-decoder vision-and-language llm decoder-only Updated Feb 7, 2025 Python cisnlp / MEXA Star 10 Code Issues Pull requests 🔍 ...
We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once. It consists of two components, i.e., a cross-decoder stacked upon a self-decoder. The self-decoder efficiently encodes global key-value (KV) caches that...
Despite the significant advancements in applying language models to the seq2seq task, there is still a lack of thorough analysis on the effectiveness of the decoder-only language model architecture. This paper aims to address this gap by conducting a detailed comparison between the encoder-decoder ...
Our method utilizes the Decoder-only architecture to determine the policy and translation concurrently. Our method alleviates the training and inference costs associated with using a Decode-only architecture. Our method attains the state-of-the-art performance on evaluation datasets. Requirements and Inst...
Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless integration of speech signals into LLMs has not been explored well. The "decoder-only" architecture has ...