Siva Reddy

Juan A. Rodriguez

Perouz Taslakian

Spandana Gella

Sai Rajeswar

ServiceNow

WebMMU Benchmark

Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (voir plus)ss isolated web-based tasks—such as website-based Visual Question Answering (VQA) and UI-to-code generation—they lack a unified evaluation suite for assessing web agents that interact with and reason about web environments. We introduce WebMMU, a large-scale benchmark for evaluating AI-driven web agents across multilingual website VQA, HTML/CSS/JavaScript code editing, and sketch-to-code generation. WebMMU provides a comprehensive evaluation suite with real-world website data, multi-step reasoning tasks, and functional UI understanding. Benchmarking state-of-the-art multimodal models on WebMMU reveals significant limitations in web-based reasoning, layout understanding, and structured code generation, particularly in preserving UI hierarchy, handling multilingual content, and producing robust, functional code. While most existing models are optimized for English-only settings, WebMMU highlights the challenges of cross-lingual adaptation in real-world web development. These findings expose critical gaps in current models’ ability to understand website structures, execute user instructions, and generate high-quality web code, underscoring the need for more advanced multimodal reasoning in AI-driven web understanding and development.

2025-03-05

ICLR.cc/2025/Workshop/DL4C (publié)

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

David Vazquez

Juan A. Rodriguez

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (voir plus)ss isolated web-based tasks—such as website-based Visual Question Answering (VQA) and UI-to-code generation—they lack a unified evaluation suite for assessing web agents that interact with and reason about web environments. We introduce WebMMU, a large-scale benchmark for evaluating AI-driven web agents across multilingual website VQA, HTML/CSS/JavaScript code editing, and sketch-to-code generation. WebMMU provides a comprehensive evaluation suite with real-world website data, multi-step reasoning tasks, and functional UI understanding. Benchmarking state-of-the-art multimodal models on WebMMU reveals significant limitations in web-based reasoning, layout understanding, and structured code generation, particularly in preserving UI hierarchy, handling multilingual content, and producing robust, functional code. While most existing models are optimized for English-only settings, WebMMU highlights the challenges of cross-lingual adaptation in real-world web development. These findings expose critical gaps in current models’ ability to understand website structures, execute user instructions, and generate high-quality web code, underscoring the need for more advanced multimodal reasoning in AI-driven web understanding and development.

2025-03-05

ICLR.cc/2025/Workshop/DL4C (publié)

Large language models deconstruct the clinical intuition behind diagnosing autism

Emmett Rabot

L. Mottron

2025-03-01

Cell (publié)

Societal Alignment Frameworks Can Improve LLM Alignment

Karolina Sta'nczak

Nicholas Meade

Mehar Bhatia

Hattie Zhou

Konstantin Böttinger

Jeremy Barnes

Jason Stanley

Jessica Montgomery

Richard Zemel

Nicolas Papernot

Nicolas Chapados

Denis Therien

Timothy P. Lillicrap

Ana Marasovi'c

Sylvie Delacroix

Gillian K. Hadfield

2025-02-27

ArXiv (prépublication)

How to Get Your LLM to Generate Challenging Problems for Evaluation

Arkil Patel

Dzmitry Bahdanau

The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional hum… (voir plus)an annotation is increasingly impracticable due to the complexities and costs involved in generating high-quality, challenging problems, particularly for tasks such as long-context reasoning. Moreover, the rapid saturation of existing human-curated benchmarks by LLMs further necessitates the need to develop scalable and automatically renewable evaluation methodologies. In this work, we introduce **CHASE**, a unified framework to synthetically generate challenging problems using LLMs without human involvement. For a given task, our approach builds a hard problem in a bottom-up manner from simpler components. Moreover since we want to generate synthetic data for evaluation, our framework decomposes the generation process into independently verifiable sub-tasks, thereby ensuring a high level of quality and correctness. We implement CHASE to create evaluation benchmarks across three diverse domains: document-based question answering, repository-level code completion, and math reasoning. The performance of state-of-the-art LLMs on these synthetic benchmarks lies in the range of 40-60\% accuracy, thereby demonstrating the effectiveness of our framework at generating hard problems. Our experiments further reveal that the Gemini models significantly outperform other LLMs at long-context reasoning, and that the performance of all LLMs drastically drops by as much as 70\% when we scale up the context size to 50k tokens.

2025-02-20

ArXiv (prépublication)

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen

Isaac Chung

Imene Kerboua

Márton Kardos

Ashwin Mathur

David Stap

Jay Gala

Wissam Siblini

Dominik Krzemiński

Genta Indra Winata

Saba Sturua

Saiteja Utpala

Mathieu Ciancone

Marion Schaeffer

Gabriel Sequeira

Diganta Misra

Shreeya Dhakal

Jonathan Rystrøm

Roman Solomatin

Ömer Veysel Çağatan … (voir 66 de plus)

Akash Kundu

Martin Bernstorff

Shitao Xiao

Akshita Sukhlecha

Bhavish Pahwa

Rafał Poświata

Kranthi Kiran GV

Shawon Ashraf

Daniel Auras

Björn Plüster

Jan Philipp Harries

Loïc Magne

Isabelle Mohr

Mariya Hendriksen

Dawei Zhu

Hippolyte Gisserot-Boukhlef

Tom Aarsen

Jan Kostkan

Konrad Wojtasik

Taemin Lee

Marek Suppa

Crystina Zhang

Roberta Rocca

Mohammed Hamdy

Andrianos Michail

John Yang

Manuel Faysse

Aleksei Vatolin

Nandan Thakur

Manan Dey

Dipam Vasani

Pranjal A Chitale

Simone Tedeschi

Nguyen Tai

Artem Snegirev

Michael Günther

Mengzhou Xia

Weijia Shi

Xing Han Lu

Jordan Clive

Gayatri K

Maksimova Anna

Silvan Wehrli

Maria Tikhonova

Henil Shalin Panchal

Aleksandr Abramov

Malte Ostendorff

Zheng Liu

Simon Clematide

Lester James Validad Miranda

Alena Fenogenova

Guangyu Song

Ruqiya Bin Safi

Wen-Ding Li

Alessia Borghini

Federico Cassano

Hongjin Su

Jimmy Lin

Howard Yen

Lasse Hansen

Sara Hooker

Chenghao Xiao

Vaibhav Adlakha

Orion Weller

Niklas Muennighoff

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address… (voir plus) these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date. Using this collection, we develop several highly multilingual benchmarks, which we use to evaluate a representative set of models. We find that while large language models (LLMs) with billions of parameters can achieve state-of-the-art performance on certain language subsets and task categories, the best-performing publicly available model is multilingual-e5-large-instruct with only 560 million parameters. To facilitate accessibility and reduce computational cost, we introduce a novel downsampling method based on inter-task correlation, ensuring a diverse selection while preserving relative model rankings. Furthermore, we optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks that drastically reduce computational demands. For instance, our newly introduced zero-shot English benchmark maintains a ranking order similar to the full-scale version but at a fraction of the computational cost.

2025-02-19

ArXiv (prépublication)

ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval

Tianyi Chen

Perouz Taslakian

Valentina Zantedeschi

2025-02-11

ArXiv (prépublication)

ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval

Tianyi Chen

Perouz Taslakian

Valentina Zantedeschi

Document retrieval is a core component of question-answering systems, as it enables conditioning answer generation on new and large-scale co… (voir plus)rpora. While effective, the standard practice of encoding documents into high-dimensional embeddings for similarity search entails large memory and compute footprints, and also makes it hard to inspect the inner workings of the system. In this paper, we propose a tree-based method for organizing and representing reference documents at various granular levels, which offers the flexibility to balance cost and utility, and eases the inspection of the corpus content and retrieval operations. Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches, hence directly optimizing for retrieval performance. Our evaluations show that ReTreever generally preserves full representation accuracy. Its hierarchical structure further provides strong coarse representations and enhances transparency by indirectly learning meaningful semantic groupings. Among hierarchical retrieval methods, ReTreever achieves the best retrieval accuracy at the lowest latency, proving that this family of techniques can be viable in practical applications.

2025-02-11

ArXiv (prépublication)

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

Juan A. Rodriguez

Xiangru Jian

Siba Smarak Panigrahi

Tianyu Zhang

Aarash Feizi

Abhay Puri

Akshay Kalkunte Suresh

François Savard

Amirhossein Abaskohi

Pierre-Andre Noel

Mats Leon Richter

Saverio Vadacchino

Shubham Agarwal

Sanket Biswas … (voir 23 de plus)

Sara Shanian

Ying Zhang

Noah Bolger

Kurt MacDonald

Simon Fauvel

Sathwik Tejaswi Madhusudhan

Srinivas Sunkara

Joao Monteiro

Krishnamurthy Dj Dvijotham

Torsten Scholak

Nicolas Chapados

Sepideh Kharaghani

Sean Hughes

M. Özsu

Issam Hadj Laradji

Perouz Taslakian

David Vazquez

Sai Rajeswar

2025-01-22

ICLR.cc/2025/Conference (poster)

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

Juan A. Rodriguez

Xiangru Jian

Siba Smarak Panigrahi

Tianyu Zhang

Aarash Feizi

Abhay Puri

Akshay Kalkunte Suresh

François Savard

Amirhossein Abaskohi

Pierre-Andre Noel

Mats Leon Richter

Saverio Vadacchino

Shubham Agarwal

Sanket Biswas … (voir 19 de plus)

Sara Shanian

Ying Zhang

Sathwik Tejaswi Madhusudhan

Joao Monteiro

Krishnamurthy Dj Dvijotham

Torsten Scholak

Nicolas Chapados

Sepideh Kharaghani

Sean Hughes

M. Özsu

Issam Hadj Laradji

Perouz Taslakian

David Vazquez

Sai Rajeswar

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (voir plus) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to relevant training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure that our data is high quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench,, a benchmark suite with 10 novel tasks where we carefully create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench, improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations revealed that participants preferred the outputs from models trained with BigDocs over those from GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning.

2025-01-22

ICLR.cc/2025/Conference (poster)

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen

Isaac Chung

Imene Kerboua

Márton Kardos

Ashwin Mathur

David Stap

Jay Gala

Wissam Siblini

Dominik Krzemiński

Genta Indra Winata

Saba Sturua

Saiteja Utpala

Mathieu Ciancone

Marion Schaeffer

Diganta Misra

Shreeya Dhakal

Jonathan Rystrøm

Roman Solomatin

Ömer Veysel Çağatan

Akash Kundu … (voir 62 de plus)

Martin Bernstorff

Shitao Xiao

Akshita Sukhlecha

Bhavish Pahwa

Rafał Poświata

Kranthi Kiran GV

Shawon Ashraf

Daniel Auras

Björn Plüster

Jan Philipp Harries

Loïc Magne

Isabelle Mohr

Dawei Zhu

Hippolyte Gisserot-Boukhlef

Tom Aarsen

Jan Kostkan

Konrad Wojtasik

Taemin Lee

Marek Suppa

Crystina Zhang

Roberta Rocca

Mohammed Hamdy

Andrianos Michail

John Yang

Manuel Faysse

Aleksei Vatolin

Nandan Thakur

Manan Dey

Dipam Vasani

Pranjal A Chitale

Simone Tedeschi

Nguyen Tai

Artem Snegirev

Mariya Hendriksen

Michael Günther

Mengzhou Xia

Weijia Shi

Xing Han Lu

Jordan Clive

Gayatri K

Maksimova Anna

Silvan Wehrli

Maria Tikhonova

Henil Shalin Panchal

Aleksandr Abramov

Malte Ostendorff

Zheng Liu

Simon Clematide

Lester James Validad Miranda

Alena Fenogenova

Guangyu Song

Ruqiya Bin Safi

Wen-Ding Li

Alessia Borghini

Federico Cassano

Lasse Hansen

Sara Hooker

Chenghao Xiao

Vaibhav Adlakha

Orion Weller

Niklas Muennighoff

Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this… (voir plus) limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-driven initiative expanding MTEB to over 500 quality-controlled evaluation tasks across 1,000+ languages. MMTEB includes a wide range of challenging novel tasks such as instruction following, long-document retrieval, and code retrieval, and represents the largest multilingual collection of evaluation tasks for embedding models to date. We use this collection to construct multiple highly multilingual benchmarks. We evaluate a representative set of models on these benchmarks. Our findings indicate that, while LLM-based models can achieve state-of-the-art performance on a subset of languages, the best-performing publicly available model across languages is the notably smaller, multilingual-e5-large-instruct. Massive benchmarks often impose high computational demands, limiting accessibility, particularly for low-resource communities. To address this, we downsample tasks based on inter-task correlation (i.e., selecting only a diverse set of tasks) while preserving relative rankings. We further optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks at a significantly lower computational cost. For instance, we introduce a new zero-shot English benchmark that maintains a similar ordering at a fraction of the cost.

2025-01-22

ICLR.cc/2025/Conference (poster)

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen

Isaac Chung

Imene Kerboua

Márton Kardos

Ashwin Mathur

David Stap

Jay Gala

Wissam Siblini

Dominik Krzemiński

Genta Indra Winata

Saba Sturua

Saiteja Utpala

Mathieu Ciancone

Marion Schaeffer

Gabriel Sequeira

Diganta Misra

Shreeya Dhakal

Jonathan Rystrøm

Roman Solomatin

Ömer Veysel Çağatan … (voir 66 de plus)

Akash Kundu

Martin Bernstorff

Shitao Xiao

Akshita Sukhlecha

Bhavish Pahwa

Rafał Poświata

Kranthi Kiran GV

Shawon Ashraf

Daniel Auras

Björn Plüster

Jan Philipp Harries

Loïc Magne

Isabelle Mohr

Mariya Hendriksen

Dawei Zhu

Hippolyte Gisserot-Boukhlef

Tom Aarsen

Jan Kostkan

Konrad Wojtasik

Taemin Lee

Marek Suppa

Crystina Zhang

Roberta Rocca

Mohammed Hamdy

Andrianos Michail

John Yang

Manuel Faysse

Aleksei Vatolin

Nandan Thakur

Manan Dey

Dipam Vasani

Pranjal A Chitale

Simone Tedeschi

Nguyen Tai

Artem Snegirev

Michael Günther

Mengzhou Xia

Weijia Shi

Xing Han Lu

Jordan Clive

Gayatri K

Maksimova Anna

Silvan Wehrli

Maria Tikhonova

Henil Shalin Panchal

Aleksandr Abramov

Malte Ostendorff

Zheng Liu

Simon Clematide

Lester James Validad Miranda

Alena Fenogenova

Guangyu Song

Ruqiya Bin Safi

Wen-Ding Li

Alessia Borghini

Federico Cassano

Hongjin Su

Jimmy Lin

Howard Yen

Lasse Hansen

Sara Hooker

Chenghao Xiao

Vaibhav Adlakha

Orion Weller

Niklas Muennighoff

Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this… (voir plus) limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-driven initiative expanding MTEB to over 500 quality-controlled evaluation tasks across 1,000+ languages. MMTEB includes a wide range of challenging novel tasks such as instruction following, long-document retrieval, and code retrieval, and represents the largest multilingual collection of evaluation tasks for embedding models to date. We use this collection to construct multiple highly multilingual benchmarks. We evaluate a representative set of models on these benchmarks. Our findings indicate that, while LLM-based models can achieve state-of-the-art performance on a subset of languages, the best-performing publicly available model across languages is the notably smaller, multilingual-e5-large-instruct. Massive benchmarks often impose high computational demands, limiting accessibility, particularly for low-resource communities. To address this, we downsample tasks based on inter-task correlation (i.e., selecting only a diverse set of tasks) while preserving relative rankings. We further optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks at a significantly lower computational cost. For instance, we introduce a new zero-shot English benchmark that maintains a similar ordering at a fraction of the cost.

2025-01-22

ICLR.cc/2025/Conference (poster)