Nicolas Chapados

Christopher Pal

Perouz Taslakian

In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference informati… (voir plus)on. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context isn't known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform ICL, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude.

2023-12-31

EMNLP (Findings) (publié)

arxiv.org

Capture the Flag: Uncovering Data Insights with Large Language Models.

Issam H. Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

David Vázquez

Christopher 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.

2023-11-06

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

Quentin Cappart

David Vázquez

Maxime Gasse

Alexandre Lacoste

2023-11-06

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

Lag-Llama: Towards Foundation Models for Probabilistic 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

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.

2023-10-31

NeurIPS.cc/2023/Workshop/R0-FoMo (poster)

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

Étienne Marcotte

Valentina Zantedeschi

Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expect… (voir plus)ation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the"region of reliability"of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

Can AI Read the Minds of Corporate Executives?

Zhenzhen Fan

Ruslan Goyenko

Issam Hadj Laradji

Fred Liu

Chengyu Zhang

2022-12-31

SSRN Electronic Journal (publié)

TACTiS: Transformer-Attentional Copulas for Time Series

Étienne Marcotte

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (voir plus)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.

2021-12-31

ICML (publié)

proceedings.mlr.press

Meta-learning framework with applications to zero-shot time-series forecasting

Boris Oreshkin

Dmitri Carpov

Yoshua Bengio

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

arxiv.org

N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting

Boris N. Oreshkin

Dmitri Carpov

Yoshua Bengio

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based o… (voir plus)n backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

2019-12-31

ICLR (publié)

Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation 1

Mohammad Havaei

Nicolas Guizard

Yoshua Bengio

2018-07-03

Signal Processing and Machine Learning for Biomedical Big Data (publié)