An Encoder-Decoder model is a fundamental architecture in the field of deep learning and natural language processing (NLP). It's widely used for a variety of tasks, including machine translation, text ...

unsupervised method to convert any pre-trained decoder-only LLM into a text encoder. LLM2Vec is very data and parameter-efficient and does not require any labeled data. There are three simple steps in ...

This repository is the official implementation of DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM. To train and evaluate the ... to the decoders of the DeTiME to generate ...

NVIDIA's TensorRT-LLM now supports encoder-decoder models with in-flight batching, offering optimized inference for AI applications. Discover the enhancements for generative AI on NVIDIA GPUs. NVIDIA ...

Within the wide range of actual applications of the Large Language Models (LLM), we can find text summarization ... risk into the objective function and modifying the encoder-decoder LLM structure by ...

NVIDIA has announced a significant update to its open-source library, TensorRT-LLM, which now includes support for encoder-decoder model architectures with in-flight batching capabilities. This ...

Typically, these approaches involve three components: a watermark encoder network, a watermark decoder network ... noteworthy benchmarking datasets for detecting LLM-generated text. One prominent ...

There are a couple of drawbacks to code-focused LLMs: they often adopt an encoder-only or decoder-only architecture ... As the team’s goal is to build a flexible code LLM suited to many different ...