3 citations · 4 across the 10 of their papers we have counts for
10 papers
Learning with the -adics
André F. T. Martins
Existing machine learning frameworks operate over the field of real numbers () and learn representations in real (Euclidean or Hilbert) vector spaces (e.g., $\mathbb{R}…
Sample-efficient Integration of New Modalities into Large Language Models
Osman Batur İnce, André F. T. Martins, Oisin Mac Aodha +1
Multimodal foundation models can process several modalities. However, since the space of possible modalities is large and evolving over time, training a model from scratch to encom…
Instituto de Telecomunicações at IWSLT 2025: Aligning Small-Scale Speech and Language Models for Speech-to-Text Learning
Giuseppe Attanasio, Sonal Sannigrahi, Ben Peters +1
This paper presents the IT-IST submission to the IWSLT 2025 Shared Task on Instruction Following Speech Processing. We submit results for the Short Track, i.e., speech recognition,…
From TOWER to SPIRE: Adding the Speech Modality to a Translation-Specialist LLM
Kshitij Ambilduke, Ben Peters, Sonal Sannigrahi +5
We introduce Spire, a speech-augmented language model (LM) capable of both translating and transcribing speech input from English into 10 other languages as well as translating tex…
AdaSplash: Adaptive Sparse Flash Attention
Nuno Gonçalves, Marcos Treviso, André F. T. Martins
The computational cost of softmax-based attention in transformers limits their applicability to long-context tasks. Adaptive sparsity, of which -entmax attention is an example,…
How Effective are State Space Models for Machine Translation?
Hugo Pitorro, Pavlo Vasylenko, Marcos Treviso +1
Transformers are the current architecture of choice for NLP, but their attention layers do not scale well to long contexts. Recent works propose to replace attention with linear re…
A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models
Peiqin Lin, André F. T. Martins, Hinrich Schütze
Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models, improving performance in both bilingual tasks, e.g., mac…
XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
Peiqin Lin, André F. T. Martins, Hinrich Schütze
Recent studies indicate that leveraging off-the-shelf or fine-tuned retrievers, capable of retrieving relevant in-context examples tailored to the input query, enhances few-shot in…
Did Translation Models Get More Robust Without Anyone Even Noticing?
Ben Peters, André F. T. Martins
Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to "noisy" inputs, such as spel…
MaLA-500: Massive Language Adaptation of Large Language Models
Peiqin Lin, Shaoxiong Ji, Jörg Tiedemann +2
Large language models (LLMs) have advanced the state of the art in natural language processing. However, their predominant design for English or a limited set of languages creates…