Siva Reddy

Biography

Siva Reddy is an assistant professor at the School of Computer Science and in the Department of Linguistics at McGill University. He completed a postdoc with the Stanford NLP Group in September 2019.

Reddy’s research goal is to enable machines with natural language understanding abilities in order to facilitate applications like question answering and conversational systems. His expertise includes building symbolic (linguistic and induced) and deep learning models for language.

Current Students

Vaibhav Adlakha

PhD - McGill University

Parishad BehnamGhader

Master's Research - McGill University

PhD - McGill University

Collaborating researcher

Gaurav Kamath

PhD - McGill University

Amirhossein Kazemnejad

Master's Research - McGill University

Aditi Khandelwal

PhD - McGill University

Principal supervisor :

PhD - McGill University

Aravind Krishnan

Collaborating Alumni - UNIVERSITÄT DES SAARLANDES

Benno Krojer

PhD - McGill University

andreas.madsen@mila.quebec

Zichao Li

PhD - McGill University

Co-supervisor :

Jackie Cheung

Xing Han Lu

PhD - McGill University

PhD - Polytechnique Montréal

Principal supervisor :

Sarath Chandar

Research Intern - McGill University

PhD - McGill University

Postdoctorate - McGill University

Arkil Patel

PhD - McGill University

Principal supervisor :

Collaborating researcher

Nooshin Soheililangroodi

Research Intern - McGill University

Karolina Ewa Stańczak

Postdoctorate - McGill University

How Do We Explain AI and Ensure the Explanation Is True? Faithfulness Measurable Models Tell You How

Ada Tur Tur

Research Intern - McGill University

Ivan Vulić

Collaborating researcher - Cambridge University

Alejandra Zambrano

Collaborating Alumni - McGill University

Blog Posts

October 1, 2024

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

Exploiting Instruction-Following Retrievers for Malicious Information Retrieval

Parishad BehnamGhader

Nicholas Meade

2025-03-11

ArXiv (preprint)

The BrowserGym Ecosystem for Web Agent Research

Thibault Le Sellier de Chezelles

Maxime Gasse

Alexandre Lacoste

Alexandre Drouin

Massimo Caccia

Léo Boisvert

Megh Thakkar

Tom Marty

Rim Assouel

Sahar Omidi Shayegan

Lawrence Keunho Jang

Xing Han Lu

Ori Yoran

Dehan Kong

Frank F. Xu

Quentin Cappart

Graham Neubig

Russ Salakhutdinov

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (see more)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.

2025-03-08

TMLR (accepted)

SafeArena: Evaluating the Safety of Autonomous Web Agents

Ada Defne Tur

Nicholas Meade

Xing Han Lu

Alejandra Zambrano

Arkil Patel

Esin Durmus

Spandana Gella

Karolina Sta'nczak

2025-03-06

ArXiv (preprint)

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

Rabiul Awal

Mahsa Massoud

Zichao Li

Aarash Feizi

Suyuchen Wang

Chris Pal

Aishwarya Agrawal

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… (see more)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 (published)

Societal Alignment Frameworks Can Improve LLM Alignment

Karolina Sta'nczak

Nicholas Meade

Mehar Bhatia

Hattie Zhou

Konstantin Bottinger

Jeremy Barnes

Jason Stanley

Jessica Montgomery

Richard Zemel

Nicolas Papernot

Denis Therien

Timothy P. Lillicrap

Ana Marasovi'c

Sylvie Delacroix

Gillian K. Hadfield

2025-02-27

ArXiv (preprint)

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… (see more)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 (preprint)

Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation

Senyu Li

Zipeng Sun

Jiayi Wang

Xue (Steve) Liu

Pontus Stenetorp

David Ifeoluwa Adelani

2025-02-17

ArXiv (preprint)

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

Shubham Gupta

Zichao Li

Tianyi Chen

Cem Subakan

Perouz Taslakian

Valentina Zantedeschi

2025-02-11

ArXiv (preprint)

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

Shubham Gupta

Zichao Li

Tianyi Chen

Cem Subakan

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… (see more)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 (preprint)

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

Ahmed Masry

Shravan Nayak

Rabiul Awal

Mahsa Massoud

Amirhossein Abaskohi

Zichao Li

Suyuchen Wang

Pierre-Andre Noel

Mats Leon Richter

Saverio Vadacchino

Shubham Agarwal

Sanket Biswas … (see 23 more)

Sara Shanian

Ying Zhang

Noah Bolger

Kurt MacDonald

Simon Fauvel

Sathwik Tejaswi Madhusudhan

Srinivas Sunkara

Joao Monteiro

Krishnamurthy Dj Dvijotham

Torsten Scholak

Sepideh Kharaghani

Sean Hughes

M. Özsu

Issam Hadj Laradji

Spandana Gella

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

Ahmed Masry

Shravan Nayak

Rabiul Awal

Mahsa Massoud

Amirhossein Abaskohi

Zichao Li

Suyuchen Wang

Pierre-Andre Noel

Mats Leon Richter

Saverio Vadacchino

Shubham Agarwal

Sanket Biswas … (see 19 more)

Sara Shanian

Ying Zhang

Sathwik Tejaswi Madhusudhan

Joao Monteiro

Krishnamurthy Dj Dvijotham

Torsten Scholak

Sepideh Kharaghani

Sean Hughes

M. Özsu

Issam Hadj Laradji

Spandana Gella

Perouz Taslakian

David Vazquez

Sai Rajeswar

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) 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

Gabriel Sequeira

Diganta Misra

Shreeya Dhakal

Jonathan Rystrøm

Roman Solomatin

Ömer Veysel Çağatan … (see 66 more)

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… (see more) 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)