Alexandre Drouin

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Sai Rajeswar

Issam Hadj Laradji

2024-07-08

ArXiv (prépublication)

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

David Vazquez

Alexandre Lacoste

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks

Léo Boisvert

Megh Thakkar

Massimo Caccia

Thibault Le Sellier De Chezelles

Alexandre Lacoste

The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recen… (voir plus)t LLMs seem capable of planning and reasoning given user instructions, their effectiveness in applying these capabilities for autonomous task solving remains underexplored. This is especially true in enterprise settings, where automated agents hold the promise of a high impact. To fill this gap, we propose WorkArena++, a novel benchmark consisting of 682 tasks corresponding to realistic workflows routinely performed by knowledge workers. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents. Our empirical studies across state-of-the-art LLMs and vision-language models (VLMs), as well as human workers, reveal several challenges for such models to serve as useful assistants in the workplace. In addition to the benchmark, we provide a mechanism to effortlessly generate thousands of ground-truth observation/action traces, which can be used for fine-tuning existing models. Overall, we expect this work to serve as a useful resource to help the community progress toward capable autonomous agents. The benchmark can be found at https://github.com/ServiceNow/WorkArena/tree/workarena-plus-plus.

2024-07-07

ArXiv (prépublication)

Gintare Karolina Dziugaite

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan

Dhanya Sridhar

Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (voir plus)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. Our analysis on four LLMs highlights that while GPT- 4 models show promising accuracy at predicting the intervention effects, they remain sensitive to distracting factors in the prompts.

2024-04-08

ArXiv (prépublication)

WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

David Vazquez

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… (voir plus)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 (prépublication)

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

David Vazquez

Alexandre Lacoste

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (voir plus)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)

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (voir plus)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 (prépublication)

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

2023-11-07

NeurIPS.cc/2023/Workshop/FMDM (publié)

The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study

Rim Assouel

Tom Marty

Massimo Caccia

Issam Hadj Laradji

Sai Rajeswar

Hector Palacios

David Vazquez

Alexandre Lacoste

2023-11-07

NeurIPS.cc/2023/Workshop/FMDM (publié)

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

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-… (voir plus)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)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Bilovs

Hena Ghonia

Nadhir Hassen

Anderson Schneider

Sahil Garg

Yuriy Nevmyvaka

Irina Rish

2023-10-12

ArXiv (prépublication)