Alessandro Sordoni

Lucas Caccia

Franccois Beaulieu

Thomas Lin

Jens Kleesiek

Paul Vozila

2025-05-15

ArXiv (prépublication)

A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment

Jean-Philippe Corbeil

Amin Dada

Jean-Michel Attendu

Asma Ben Abacha

Lucas Caccia

Franccois Beaulieu

Thomas Lin

Jens Kleesiek

Paul Vozila

High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language… (voir plus) models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation, which remains challenging. An additional bottleneck is the unavailability and high sensitivity of clinical data. To address these challenges, we propose a novel framework for adapting SLMs into high-performing clinical models. We introduce the MediPhi collection of 3.8B-parameter SLMs developed with our novel framework: pre-instruction tuning of experts on relevant medical and clinical corpora (PMC, Medical Guideline, MedWiki, etc.), model merging, and clinical-tasks alignment. To cover most clinical tasks, we extended the CLUE benchmark to CLUE+, doubling its size. Our expert models deliver relative improvements on this benchmark over the base model without any task-specific fine-tuning: 64.3% on medical entities, 49.5% on radiology reports, and 44% on ICD-10 coding (outperforming GPT-4-0125 by 14%). We unify the expert models into MediPhi via model merging, preserving gains across benchmarks. Furthermore, we built the MediFlow collection, a synthetic dataset of 2.5 million high-quality instructions on 14 medical NLP tasks, 98 fine-grained document types, and JSON format support. Alignment of MediPhi using supervised fine-tuning and direct preference optimization achieves further gains of 18.9% on average.

2025-05-15

ArXiv (prépublication)

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

Kusha Sareen

Morgane M Moss

Rishabh Agarwal

Arian Hosseini

2025-05-07

ArXiv (prépublication)

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

Kusha Sareen

Morgane M Moss

Rishabh Agarwal

Arian Hosseini

2025-05-07

ArXiv (prépublication)

A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning

Prateek Yadav

Colin Raffel

Mohammed Muqeeth

Lucas Caccia

Haokun Liu

Tianlong Chen

Mohit Bansal

Leshem Choshen

The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particula… (voir plus)r domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.

2025-04-25

TMLR (accepté)

doi.org

debug-gym: A Text-Based Environment for Interactive Debugging

Xingdi Yuan

Morgane M Moss

Charbel Feghali

Chinmay Singh

Darya Moldavskaya

Drew MacPhee

Lucas Caccia

Matheus Pereira

Minseon Kim

Marc-Alexandre Côté

2025-03-27

ArXiv (prépublication)

debug-gym: A Text-Based Environment for Interactive Debugging

Xingdi Yuan

Morgane M Moss

Charbel Feghali

Chinmay Singh

Darya Moldavskaya

Drew MacPhee

Lucas Caccia

Matheus Pereira

Minseon Kim

Marc-Alexandre Côté

2025-03-27

ArXiv (prépublication)

doi.org

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts

Samin Yeasar Arnob

Zhan Su

Minseon Kim

Oleksiy Ostapenko

Doina Precup

Lucas Caccia

Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be rapidly a… (voir plus)dapted on the fly for specific downstream tasks, without requiring additional fine-tuning. Typically, LoRA (Low-Rank Adaptation) serves as the foundational building block of such parameter-efficient modular architectures, leveraging low-rank weight structures to reduce the number of trainable parameters. In this paper, we study the properties of sparse adapters, which train only a subset of weights in the base neural network, as potential building blocks of modular architectures. First, we propose a simple method for training highly effective sparse adapters, which is conceptually simpler than existing methods in the literature and surprisingly outperforms both LoRA and full fine-tuning in our setting. Next, we investigate the merging properties of these sparse adapters by merging adapters for up to 20 natural language processing tasks, thus scaling beyond what is usually studied in the literature. Our findings demonstrate that sparse adapters yield superior in-distribution performance post-merging compared to LoRA or full model merging. Achieving strong held-out performance remains a challenge for all methods considered.

2025-03-05

ICLR.cc/2025/Workshop/MCDC (accepté)

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts

Samin Yeasar Arnob

Zhan Su

Minseon Kim

Oleksiy Ostapenko

Doina Precup

Lucas Caccia

Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be rapidly a… (voir plus)dapted on the fly for specific downstream tasks, without requiring additional fine-tuning. Typically, LoRA (Low-Rank Adaptation) serves as the foundational building block of such parameter-efficient modular architectures, leveraging low-rank weight structures to reduce the number of trainable parameters. In this paper, we study the properties of sparse adapters, which train only a subset of weights in the base neural network, as potential building blocks of modular architectures. First, we propose a simple method for training highly effective sparse adapters, which is conceptually simpler than existing methods in the literature and surprisingly outperforms both LoRA and full fine-tuning in our setting. Next, we investigate the merging properties of these sparse adapters by merging adapters for up to 20 natural language processing tasks, thus scaling beyond what is usually studied in the literature. Our findings demonstrate that sparse adapters yield superior in-distribution performance post-merging compared to LoRA or full model merging. Achieving strong held-out performance remains a challenge for all methods considered.

2025-03-05

ICLR.cc/2025/Workshop/MCDC (accepté)

Training Plug n' Play Knowledge Modules with Deep Context Distillation

Lucas Caccia

Alan Ansell

Ivan Vulić

Edoardo Ponti

Dynamically integrating new or rapidly evolving information after Language Model (LM) pre-training remains challenging, particularly in low-… (voir plus)data scenarios or when dealing with private and specialized documents. In-context learning and retrieval-augmented generation (RAG) face limitations, including their high inference costs and their inability to capture global document information. In this paper, we propose a way of modularizing knowledge by training Knowledge Modules (KMs). KMs are lightweight components implemented as parameter-efficient LoRA modules, which are trained to store information about new documents and can be easily plugged into models on demand. We show that next-token prediction performs poorly in training KMs. We instead propose Deep Context Distillation: we learn KMs parameters such as to simulate hidden states and logits of a teacher that takes the document in context. Our method outperforms standard next-token prediction and pre-instruction training techniques, across two datasets. Finally, we highlight synergies between KMs and retrieval-augmented generation.

2025-03-05

ICLR.cc/2025/Workshop/MCDC (accepté)