15 citations · 51 across the 14 of their papers we have counts for
16 papers
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier +29
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. T…
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages
Silvia Severini, Ayyoob Imani, Philipp Dufter +1
Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE data…
Graph Algorithms for Multiparallel Word Alignment
Ayyoob Imani, Masoud Jalili Sabet, Lütfi Kerem Şenel +3
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research…
Wine is Not v i n. -- On the Compatibility of Tokenizations Across Languages
Antonis Maronikolakis, Philipp Dufter, Hinrich Schütze
The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements. Typically, subword tokenization…
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus
Ayyoob Imani, Masoud Jalili Sabet, Philipp Dufter +2
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective. Researching typological prope…
Static Embeddings as Efficient Knowledge Bases?
Philipp Dufter, Nora Kassner, Hinrich Schütze
Recent research investigates factual knowledge stored in large pretrained language models (PLMs). Instead of structural knowledge base (KB) queries, masked sentences such as "Paris…