Jun 17, 2023 · Delve into Transformer architectures: from the original encoder-decoder structure, to BERT & RoBERTa encoder-only models, to the GPT series focused on decoding. Explore their evolution, strengths, & applications in NLP tasks.

Feb 3, 2024 · In a translation task, an encoder takes an English sentence and converts it into a vector that represents its linguistic features and meaning. Decoder: Takes the encoded representation and...

LLMs中有的是只有编码器encoder-only，有的只有解码器decoder-only，有的是2者混合 encoder decoder hybrid。 三者都属于 Seq2Seq ，sequence to sequence。 并且字面意思是虽只有编码器encoder，实际上LLMs是能decoder一些文本和token的，也算是decoder。

Apr 27, 2024 · Decoder-based LLMs can be broadly classified into three main types: encoder-decoder, causal decoder, and prefix decoder. Each architecture type exhibits distinct attention patterns. Encoder-Decoder Architecture

Apr 8, 2023 · This paper aims to address this gap by conducting a detailed comparison between the encoder-decoder architecture and the decoder-only language model framework through the analysis of a regularized encoder-decoder structure.

May 4, 2024 · Some others, like T5, have both an encoder and a decoder, with some small modifications on the architecture and training strategy. Why some LLMs took only a part of the original transformer...

Apr 9, 2024 · In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning.

Sep 29, 2024 · In this blog, we’ll explore the architecture of Transformer-based Large Language Models (LLMs), focusing on the encoder-decoder structure. Although this analysis centers on the encoder-decoder...

Sep 22, 2024 · Encoder-Only vs Decoder-Only: Uncovering the Distinctions. Let’s highlight the key differences between encoder-only and decoder-only architectures: Use Cases: Encoder-only models are well-suited for predictive modeling tasks, leveraging embeddings for classification.

The Encoder-decoder architecture of LLMs is built upon the traditional Transformer Encoder-decoder architecture. The Encoder-decoder architecture consists of two main components: the Encoder and the Decoder.