13 citations · 32 across the 15 of their papers we have counts for
19 papers
DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents
Sergio Burdisso, Esaú Villatoro-Tello, Dairazalia Sánchez-Cortés +4
Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic. While factual hallucination has received extensive attention, a…
Steering Speech-Language Models: Training-Free Task Specialization via Contrastive Activation Addition
Séverin Baroudi, Yanis Labrak, Pierfrancesco Melucci +5
Activation steering has proven effective for controlling the behavior of Large Language Models (LLMs) at inference time, but its application to SpeechLLMs remains new, and training…
When Synthetic Speech Is All You Have: Better Call GRPO
Shashi Kumar, Yanis Labrak, Hasindri Watawana +5
LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TT…
How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso +9
In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is a…
Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
Yanis Labrak, David Grünert, Séverin Baroudi +11
Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open-ended generation tasks most relevant…
DASHA: Diarization-Aware Speech-LLM ASR for Healthcare in Code-Switched Indic Doctor-Patient Conversations
Séverin Baroudi, Yanis Labrak, Shashi Kumar +7
Extracting patients' medical conditions from recorded primary-care conversations requires knowing who said what, which is difficult when speech is far-field, spontaneous, overlappi…
An Empirical Analysis of Discrete Unit Representations in Speech Language Modeling Pre-training
Yanis Labrak, Richard Dufour, Mickaël Rouvier
This paper investigates discrete unit representations in Speech Language Models (SLMs), focusing on optimizing speech modeling during continual pre-training. In this paper, we syst…
LFAR: Accounting for Layerwise Dynamics to Improve Multimodal Adaptation of Language Models
Santiago Cuervo, Adel Moumen, Yanis Labrak +5
Text-pretrained language models (LMs) encode rich world knowledge, but adapting them to process and generate perceptual modalities such as audio and images while effectively levera…
Zero-Shot End-To-End Spoken Question Answering In Medical Domain
Yanis Labrak, Adel Moumen, Richard Dufour +1
In the rapidly evolving landscape of spoken question-answering (SQA), the integration of large language models (LLMs) has emerged as a transformative development. Conventional appr…
Synthetic Lyrics Detection Across Languages and Genres
Yanis Labrak, Markus Frohmann, Gabriel Meseguer-Brocal +1
In recent years, the use of large language models (LLMs) to generate music content, particularly lyrics, has gained in popularity. These advances provide valuable tools for artists…
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain
Yanis Labrak, Adrien Bazoge, Oumaima El Khettari +8
The biomedical domain has sparked a significant interest in the field of Natural Language Processing (NLP), which has seen substantial advancements with pre-trained language models…
How Important Is Tokenization in French Medical Masked Language Models?
Yanis Labrak, Adrien Bazoge, Beatrice Daille +2
Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trai…
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
Yanis Labrak, Adrien Bazoge, Emmanuel Morin +3
Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. D…
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
Yanis Labrak, Mickael Rouvier, Richard Dufour
We evaluate four state-of-the-art instruction-tuned large language models (LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13 real-world clinical and biomedic…
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains
Yanis Labrak, Adrien Bazoge, Richard Dufour +4
In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on…
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain
Yanis Labrak, Adrien Bazoge, Richard Dufour +4
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Jason Alan Fries, Leon Weber, Natasha Seelam +40
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance. Natural language prompt…
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Qingyu Chen, Alexis Allot, Robert Leaman +35
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design…