7 citations · 27 across the 8 of their papers we have counts for
8 papers
Studying quantization trade-offs for efficient inference deployment in machine translation
Jim Zhao, Sohir Maskey, Koen Oostermeijer +2
Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latency. Quantization is a common…
Accuracy and Normalized Accuracy under Length Bias: Analysis, Guidelines, and a Bayesian Alternative
Koen Oostermeijer
Multiple-choice benchmarks that rank candidate completions by conditional log-probability suffer from a length bias: because log-probabilities sum over tokens, longer answers tend…
A Family of LLMs Liberated from Static Vocabularies
Aleph Alpha, :, Adnen Abdessaied +35
Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although lea…
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
Alex Havrilla, Andrew Dai, Laura O'Mahony +17
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct compariso…
Quality-Diversity through AI Feedback
Herbie Bradley, Andrew Dai, Hannah Teufel +7
In many text-generation problems, users may prefer not only a single response, but a diverse range of high-quality outputs from which to choose. Quality-diversity (QD) search algor…
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
Marco Bellagente, Manuel Brack, Hannah Teufel +11
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users. The intended generation can be expressed in…
M-VADER: A Model for Diffusion with Multimodal Context
Samuel Weinbach, Marco Bellagente, Constantin Eichenberg +7
We introduce M-VADER: a diffusion model (DM) for image generation where the output can be specified using arbitrary combinations of images and text. We show how M-VADER enables the…
Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain
Koen Oostermeijer, Qing Wang, Jun Du
One of the strengths of traditional convolutional neural networks (CNNs) is their inherent translational invariance. However, for the task of speech enhancement in the time-frequen…