33 citations · 94 across the 28 of their papers we have counts for
32 papers
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support
John Mendonça, Daniel Tschernutter, José Pombal +6
With people increasingly turning to AI chatbots for mental health support, the need to evaluate these tools is becoming more pressing. Recent benchmarks attempt to mimic a realisti…
Move by Move: Measuring and Steering How LLMs Conduct Psychotherapy
Afonso Baldo, Hugo Pitorro, Areti Vassilopoulos +5
As the demand for mental health services outpaces clinical workforce capacity, users are increasingly turning to large language models (LLMs) for accessible and private support. Al…
Risk Governance for Generative AI Mental Health Support: A Multi-Turn Safety Architecture
Anabela C. Areias, Catarina Botelho, António Farinhas +7
Large language models (LLMs) are increasingly used for emotional support despite lacking mechanisms to safely govern evolving mental health risk. Existing safety approaches primari…
EuroLLM-22B: Technical Report
Miguel Moura Ramos, Duarte M. Alves, Hippolyte Gisserot-Boukhlef +15
This report presents EuroLLM-22B, a large language model trained from scratch to support the needs of European citizens by covering all 24 official European Union languages and 11…
MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support
António Farinhas, Nuno M. Guerreiro, José Pombal +6
Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose s…
MindEval: Benchmarking Language Models on Multi-turn Mental Health Support
José Pombal, Maya D'Eon, Nuno M. Guerreiro +3
Demand for mental health support through AI chatbots is surging, though current systems present several limitations, like sycophancy or overvalidation, and reinforcement of maladap…
TowerVision: Understanding and Improving Multilinguality in Vision-Language Models
André G. Viveiros, Patrick Fernandes, Saul Santos +7
Despite significant advances in vision-language models (VLMs), most existing work follows an English-centric design process, limiting their effectiveness in multilingual settings.…
Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs
Ricardo Rei, Nuno M. Guerreiro, José Pombal +4
Fine-tuning pretrained LLMs has been shown to be an effective strategy for reaching state-of-the-art performance on specific tasks like machine translation. However, this process o…
EuroLLM-9B: Technical Report
Pedro Henrique Martins, João Alves, Patrick Fernandes +14
This report presents EuroLLM-9B, a large language model trained from scratch to support the needs of European citizens by covering all 24 official European Union languages and 11 a…
Zero-shot Benchmarking: A Framework for Flexible and Scalable Automatic Evaluation of Language Models
José Pombal, Nuno M. Guerreiro, Ricardo Rei +1
As language models improve and become capable of performing more complex tasks across modalities, evaluating them automatically becomes increasingly challenging. Developing strong…
Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation
José Pombal, Nuno M. Guerreiro, Ricardo Rei +1
As automatic metrics become increasingly stronger and widely adopted, the risk of unintentionally "gaming the metric" during model development rises. This issue is caused by metric…
EuroBERT: Scaling Multilingual Encoders for European Languages
Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves +16
General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models. Despite their w…
Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral
António Farinhas, Nuno M. Guerreiro, Sweta Agrawal +2
Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some i…
Analyzing Context Contributions in LLM-based Machine Translation
Emmanouil Zaranis, Nuno M. Guerreiro, André F. T. Martins
Large language models (LLMs) have achieved state-of-the-art performance in machine translation (MT) and demonstrated the ability to leverage in-context learning through few-shot ex…
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
Sweta Agrawal, José G. C. de Souza, Ricardo Rei +5
Alignment with human preferences is an important step in developing accurate and safe large language models. This is no exception in machine translation (MT), where better handling…
Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
Hippolyte Gisserot-Boukhlef, Ricardo Rei, Emmanuel Malherbe +3
Neural metrics for machine translation (MT) evaluation have become increasingly prominent due to their superior correlation with human judgments compared to traditional lexical met…
EuroLLM: Multilingual Language Models for Europe
Pedro Henrique Martins, Patrick Fernandes, João Alves +12
The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at develo…
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
Marcos Treviso, Nuno M. Guerreiro, Sweta Agrawal +7
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these e…
Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
Duarte M. Alves, José Pombal, Nuno M. Guerreiro +10
While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only wh…
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation
Anas Himmi, Guillaume Staerman, Marine Picot +2
Hallucinated translations pose significant threats and safety concerns when it comes to the practical deployment of machine translation systems. Previous research works have identi…
CroissantLLM: A Truly Bilingual French-English Language Model
Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro +13
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully op…
Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning
Duarte M. Alves, Nuno M. Guerreiro, João Alves +5
Large language models (LLMs) are a promising avenue for machine translation (MT). However, current LLM-based MT systems are brittle: their effectiveness highly depends on the choic…
xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection
Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt +3
Widely used learned metrics for machine translation evaluation, such as COMET and BLEURT, estimate the quality of a translation hypothesis by providing a single sentence-level scor…
Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task
Ricardo Rei, Nuno M. Guerreiro, José Pombal +5
We present the joint contribution of Unbabel and Instituto Superior Técnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: sentence- an…