Portrait of Nicolas Chapados

Nicolas Chapados

Associate Industry Member
Adjunct Professor, Polytechnique Montréal, Department of Applied Mathematics
Vice-President, Research, ServiceNow Research
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Biography

Nicolas Chapados is VP of research at ServiceNow Inc. He holds an engineering degree from McGill University and a PhD in computer science from Université de Montréal. In 2021, while still writing his thesis, Chapados and his advisor Yoshua Bengio co-founded ApSTAT Technologies, a machine learning technology transfer firm that applies cutting-edge academic research ideas to areas like insurance risk evaluation, supply chain planning, business forecasting, biotechnology and hedge fund management. He then went on to co-found a number of spin-off companies: Imagia, which focuses on the AI analysis of medical images to detect and quantify cancer early; Element AI, which was acquired by ServiceNow in January 2021; and Chapados Couture Capital, a quantitative asset manager. Chapados’ research interests include time series modelling, natural language processing and optimal decision-making. He holds the Chartered Financial Analyst (CFA) designation.

Publications

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader
Vaibhav Adlakha
Marius Mosbach
Dzmitry Bahdanau
Nicolas Chapados
Siva Reddy
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is… (see more) only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. We demonstrate the effectiveness of LLM2Vec by applying it to 3 popular LLMs ranging from 1.3B to 7B parameters and evaluate the transformed models on English word- and sequence-level tasks. We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). Moreover, when combining LLM2Vec with supervised contrastive learning, we achieve state-of-the-art performance on MTEB among models that train only on publicly available data. Our strong empirical results and extensive analysis demonstrate that LLMs can be effectively transformed into universal text encoders in a parameter-efficient manner without the need for expensive adaptation or synthetic GPT-4 generated data.
2024-04-09
ArXiv (preprint)
doi.org
arxiv.org
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader
Vaibhav Adlakha
Marius Mosbach
Dzmitry Bahdanau
Nicolas Chapados
Siva Reddy
2024-04-09
ArXiv (preprint)
doi.org
arxiv.org
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (see more)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
2024-03-12
ArXiv (preprint)
doi.org
arxiv.org
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (see more)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
2024-03-11
ICLR.cc/2024/Workshop/LLMAgents (poster)
doi.org
openreview.net
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov
Raymond Li
Loubna Ben allal
Federico Cassano
Joel Lamy-Poirier
Nouamane Tazi
Ao Tang
Dmytro Pykhtar
Jiawei Liu
Yuxiang Wei
Tianyang Liu
Max Tian
Denis Kocetkov
Arthur Zucker
Younes Belkada
Zijian Wang
Qian Liu
Dmitry Abulkhanov
Indraneil Paul
Zhuang Li … (see 46 more)
Wen-Ding Li
Megan L. Risdal
Jia LI
Jian Zhu
Terry Yue Zhuo
Evgenii Zheltonozhskii
Nii Osae Osae Dade
Wenhao Yu
Lucas Krauss
Naman Jain
Yixuan Su
Xuanli He
Manan Dey
Edoardo Abati
Yekun Chai
Niklas Muennighoff
Xiangru Tang
Muhtasham Oblokulov
Christopher Akiki
Marc Marone
Chenghao Mou
Mayank Mishra
Alex Gu
Binyuan Hui
Tri Dao
Armel Zebaze
Olivier Dehaene
Nicolas Patry
Canwen Xu
Julian McAuley
Han Hu
Torsten Scholak
Sebastien Paquet
Jennifer Robinson
Carolyn Jane Anderson
Nicolas Chapados
Mostofa Ali Patwary
Nima Tajbakhsh
Yacine Jernite
Carlos Muñoz Ferrandis
Lingming Zhang
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
2024-02-29
ArXiv (preprint)
doi.org
arxiv.org
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov
Raymond Li
Loubna Ben allal
Federico Cassano
Joel Lamy-Poirier
Nouamane Tazi
Ao Tang
Dmytro Pykhtar
Jiawei Liu
Yuxiang Wei
Tianyang Liu
Max Tian
Denis Kocetkov
Arthur Zucker
Younes Belkada
Zijian Wang
Qian Liu
Dmitry Abulkhanov
Indraneil Paul
Zhuang Li … (see 46 more)
Wen-Ding Li
Megan L. Risdal
Jia LI
Jian Zhu
Terry Yue Zhuo
Evgenii Zheltonozhskii
Nii Osae Osae Dade
Wenhao Yu
Lucas Krauss
Naman Jain
Yixuan Su
Xuanli He
Manan Dey
Edoardo Abati
Yekun Chai
Niklas Muennighoff
Xiangru Tang
Muhtasham Oblokulov
Christopher Akiki
Marc Marone
Chenghao Mou
Mayank Mishra
Alex Gu
Binyuan Hui
Tri Dao
Armel Zebaze
Olivier Dehaene
Nicolas Patry
Canwen Xu
Julian McAuley
Han Hu
Torsten Scholak
Sebastien Paquet
Jennifer Robinson
Carolyn Jane Anderson
Nicolas Chapados
Mostofa Ali Patwary
Nima Tajbakhsh
Yacine Jernite
Carlos Muñoz Ferrandis
Lingming Zhang
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
2024-02-29
ArXiv (preprint)
doi.org
arxiv.org
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Nicolas Chapados
Alexandre Drouin
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (see more)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Capture the Flag: Uncovering Data Insights with Large Language Models
Issam Hadj Laradji
Perouz Taslakian
Sai Rajeswar
Valentina Zantedeschi
Alexandre Lacoste
Nicolas Chapados
David Vazquez
Chris Pal
Alexandre Drouin
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (see more)er, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a"capture the flag"principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.
2023-12-21
ArXiv (preprint)
doi.org
arxiv.org
Capture the Flag: Uncovering Data Insights with Large Language Models
Issam Hadj Laradji
Perouz Taslakian
Sai Rajeswar
Valentina Zantedeschi
Alexandre Lacoste
Nicolas Chapados
David Vazquez
Chris Pal
Alexandre Drouin
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (published)
doi.org
openreview.net
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Sai Rajeswar
Hector Palacios
Quentin Cappart
David Vazquez
Nicolas Chapados
Maxime Gasse
Alexandre Lacoste
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (published)
openreview.net
openreview.net
Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Arian Khorasani
George Adamopoulos
Rishika Bhagwatkar
Marin Biloš
Hena Ghonia
Nadhir Hassen
Anderson Schneider
Sahil Garg
Alexandre Drouin
Nicolas Chapados
Yuriy Nevmyvaka
Irina Rish
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (see more)Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.
2023-11-01
NeurIPS.cc/2023/Workshop/R0-FoMo (poster)
doi.org
openreview.net