Jun 17, 2023 · However, the main difference is that encoders are designed to learn embeddings that can be used for various predictive modeling tasks such as classification. In contrast, decoders are designed to generate new texts, for example, answering user queries.

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.

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...

Feb 3, 2024 · Causal Decoder (CD) vs Encoder-Decoder (ED) The performance of decoder-only, also referred to as causal decoder, against encoder-decoder models has long been studied.

Mar 22, 2024 · At the heart of these sophisticated models are two fundamental concepts: encoders and embeddings. These elements play critical roles in how LLMs understand and process the vast seas of textual...

Apr 27, 2024 · In this comprehensive guide, we will explore the inner workings of decoder-based LLMs, delving into the fundamental building blocks, architectural innovations, and implementation details that have propelled these models to the forefront of NLP research and applications.

Apr 8, 2023 · Interpreting Language Model as a Regularized Encoder-Decoder, by Zihao Fu and 6 other authors. The sequence-to-sequence (seq2seq) task aims at generating the target sequence based on the given input source sequence.

Large Language Model architectures can be categorized by encoder-decoder models, encoder-only, decoder-only, and mixture of experts (MoE) models. Each architecture boasts unique strengths but isn't confined strictly to its optimal uses.

Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend.

Gain a comprehensive understanding of LLM architecture; Construct and deploy real-world applications using LLMs; Learn the fundamentals of search and retrieval for AI applications; Understand encoder and decoder models at a deep level; Train, fine-tune, and deploy LLMs for enterprise use cases; Implement RAG-based architectures with open-source ...