David Vázquez

Valentina Zantedeschi

Alexandre Lacoste

Christopher Pal

Sai Rajeswar Mudumba

Issam Hadj Laradji

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We intro… (see more)duce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3 as an effective, open-source evaluator to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive automated data analytics and can be accessed here: https://github.com/ServiceNow/insight-bench.

2025-01-21

ICLR.cc/2025/Conference (poster)

Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection

Charles Guille-escuret

Pierre-Andre Noel

Ioannis Mitliagkas

Joao Monteiro

Improving the reliability of deployed machine learning systems often involves developing methods to detect out-of-distribution (OOD) inputs.… (see more) However, existing research often narrowly focuses on samples from classes that are absent from the training set, neglecting other types of plausible distribution shifts. This limitation reduces the applicability of these methods in real-world scenarios, where systems encounter a wide variety of anomalous inputs. In this study, we categorize five distinct types of distribution shifts and critically evaluate the performance of recent OOD detection methods on each of them. We publicly release our benchmark under the name BROAD (Benchmarking Resilience Over Anomaly Diversity). Our findings reveal that while these methods excel in detecting unknown classes, their performance is inconsistent when encountering other types of distribution shifts. In other words, they only reliably detect unexpected inputs that they have been specifically designed to expect. As a first step toward broad OOD detection, we learn a generative model of existing detection scores with a Gaussian mixture. By doing so, we present an ensemble approach that offers a more consistent and comprehensive solution for broad OOD detection, demonstrating superior performance compared to existing methods. Our code to download BROAD and reproduce our experiments is publicly available.

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

Repliqa: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

Joao Monteiro

Pierre-Andre Noel

Étienne Marcotte

Sai Rajeswar

Valentina Zantedeschi

Christopher Pal

Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includ… (see more)es encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (e.g., a news article) absent from the internet; (2) a question about the document's topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

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

Maxime Gasse

Massimo Caccia

Issam H. Laradji

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 33 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.

2024-07-07

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

proceedings.mlr.press

Exploring validation metrics for ofﬂine model-based optimisation

Christopher Beckham

Alexandre Piché

Christopher Pal

In ofﬂine model-based optimisation (MBO) we are interested in using machine learning to de-sign candidates that maximise some measure of d… (see more)esirability through an expensive but real-world scoring process. Ofﬂine MBO tries to approximate this expensive scoring function and use that to evaluate generated designs, however evaluation is non-exact because one approximation is being evaluated with another. Instead, we ask ourselves: if we did have the real world scoring function at hand, what cheap-to-compute validation metrics would correlate best with this? Since the real-world scoring function is available for simulated MBO datasets, insights obtained from this can be transferred over to real-world ofﬂine MBO tasks where the real-world scoring function is expensive to compute. To address this, we propose a conceptual evaluation framework that is amenable to measuring extrapolation, and apply this to conditional denoising diffusion models. Empirically, we ﬁnd that two validation metrics – agreement and Frechet distance – correlate quite well with the ground truth. When there is high variability in conditional generation, feedback is required in the form of an approximated version of the real-world scoring function. Furthermore, we ﬁnd that generating high-scoring samples may require heavily weighting the generative model in favour of sample quality, potentially at the cost of sample diversity.

2024-06-12

TMLR (accepted)

IntentGPT: Few-shot Intent Discovery with Large Language Models

Juan A. Rodriguez

Nicholas Botzer

Christopher Pal

Marco Pedersoli

Issam Hadj Laradji

2024-03-10

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

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference

Joao Monteiro

Étienne Marcotte

Pierre-Andre Noel

Valentina Zantedeschi

Christopher Pal

In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference informati… (see more)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) (published)

arxiv.org

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

Issam H. Laradji

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

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… (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.

2023-11-06

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

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

Maxime Gasse

Alexandre Lacoste

2023-11-06

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

OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning

Pau Rodríguez

A key aspect of human intelligence is the ability to imagine -- composing learned concepts in novel ways -- to make sense of new scenarios. … (see more)Such capacity is not yet attained for machine learning systems. In this work, in the context of visual reasoning, we show how modularity can be leveraged to derive a compositional data augmentation framework inspired by imagination. Our method, denoted Object-centric Compositional Neural Module Network (OC-NMN), decomposes visual generative reasoning tasks into a series of primitives applied to objects without using a domain-specific language. We show that our modular architectural choices can be used to generate new training tasks that lead to better out-of-distribution generalization. We compare our model to existing and new baselines in proposed visual reasoning benchmark that consists of applying arithmetic operations to MNIST digits.

2023-10-27

ArXiv (preprint)

arxiv.org

GEO-Bench: Toward Foundation Models for Earth Monitoring

Alexandre Lacoste

Nils Lehmann

Pau Rodríguez

Evan David Sherwin

Hannah Kerner

Björn Lütjens

Jeremy Irvin

David Dao

Hamed Alemohammad

Mehmet Gunturkun

Dava Newman

Stefano Ermon

Xiao Xiang Zhu

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning

Charles Guille-escuret

Pau Rodríguez

Ioannis Mitliagkas

Joao Monteiro

Handling out-of-distribution (OOD) samples has become a major stake in the real-world deployment of machine learning systems. This work expl… (see more)ores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples: unseen classes and adversarial perturbations. First, we pair self-supervised contrastive learning with the maximum mean discrepancy (MMD) two-sample test. This approach enables us to robustly test whether two independent sets of samples originate from the same distribution, and we demonstrate its effectiveness by discriminating between CIFAR-10 and CIFAR-10.1 with higher confidence than previous work. Motivated by this success, we introduce CADet (Contrastive Anomaly Detection), a novel method for OOD detection of single samples. CADet draws inspiration from MMD, but leverages the similarity between contrastive transformations of a same sample. CADet outperforms existing adversarial detection methods in identifying adversarially perturbed samples on ImageNet and achieves comparable performance to unseen label detection methods on two challenging benchmarks: ImageNet-O and iNaturalist. Significantly, CADet is fully self-supervised and requires neither labels for in-distribution samples nor access to OOD examples.

2023-09-20

NeurIPS.cc/2023/Conference (poster)