Siva Reddy

Biographie

Siva Reddy est professeur adjoint en informatique et linguistique à l’Université McGill. Ses travaux portent sur les algorithmes qui permettent aux ordinateurs de comprendre et de traiter les langues humaines. Il a fait ses études postdoctorales avec le Stanford NLP Group. Son expertise inclut la construction de symboliques linguistiques et induites et de modèles d’apprentissage profond pour le langage.

Étudiants actuels

Vaibhav Adlakha

Doctorat - McGill

Parishad BehnamGhader

Maîtrise recherche - McGill

Doctorat - McGill

Collaborateur·rice de recherche - McGill

Verna Dankers

Postdoctorat - University of Edinburgh

Jiaqi Deng

Collaborateur·rice de recherche

Charbel El Feghali

Stagiaire de recherche - McGill

Desmond Elliott

Visiteur de recherche indépendant

Co-superviseur⋅e :

Yoshua Bengio

Jay Gala

Maîtrise recherche - McGill

Co-superviseur⋅e :

Collaborateur·rice de recherche

Hanseok Hanseok Oh

Collaborateur·rice alumni

Doctorat - McGill

Co-superviseur⋅e :

Timothy O'Donnell

Imene Kerboua

Collaborateur·rice de recherche - INSA Lyon, France

Doctorat - McGill

Superviseur⋅e principal⋅e :

Golnoosh Farnadi

Austin Kraft

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Zichao Li

Doctorat - McGill

Co-superviseur⋅e :

Jackie Cheung

Fengyuan Liu

Maîtrise recherche - McGill

Co-superviseur⋅e :

Dzmitry Bahdanau

Xing Han Lu

Doctorat - McGill

Maîtrise recherche - McGill

Doctorat - McGill

Postdoctorat - McGill

Marzia Nouri

Maîtrise recherche - McGill

Arkil Patel

Doctorat - McGill

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - N/A

Ben Saine

Stagiaire de recherche - McGill

Dongchan Shin

Collaborateur·rice alumni

Karolina Ewa Stańczak

Collaborateur·rice alumni - McGill

Ivan Titov

Collaborateur·rice de recherche

Co-superviseur⋅e :

Yoshua Bengio

Ada Tur

Stagiaire de recherche - McGill

Doctorat - McGill

Collaborateur·rice alumni - McGill

Donghao Zeng

Stagiaire de recherche - McGill

Comment expliquer l’IA et s’assurer que cette explication est vraie? Les modèles mesurables de fidélité vous indiquent comment y parvenir

Billets de blogue

1 octobre 2024

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

Not All Data Are Unlearned Equally

Aravind Krishnan

Marius Mosbach

Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context… (voir plus) of large language models (LLMs), unlearning has recently received increased attention, particularly for removing knowledge about named entities from models for privacy purposes. While various approaches have been proposed to address the unlearning problem, most existing approaches treat all data points to be unlearned equally, i.e., unlearning that Montreal is a city in Canada is treated exactly the same as unlearning the phone number of the first author of this paper. In this work, we show that this all data is equal assumption does not hold for LLM unlearning. We study how the success of unlearning depends on the frequency of the knowledge we want to unlearn in the pre-training data of a model and find that frequency strongly affects unlearning, i.e., more frequent knowledge is harder to unlearn. Additionally, we uncover a misalignment between probability and generation-based evaluations of unlearning and show that this problem worsens as models become larger. Overall, our experiments highlight the need for better evaluation practices and novel methods for LLM unlearning that take the training data of models into account.

2025-07-06

colmweb.org/COLM/2025/Conference (accepté)

DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning

Sara Vera Marjanovi'c

Arkil Patel

Vaibhav Adlakha

Milad Aghajohari

Parishad BehnamGhader

Amirhossein Kazemnejad

Gaurav Kamath

Marius Mosbach

Karolina Stanczak

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (voir plus)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.

2025-04-01

ArXiv (prépublication)

Societal Alignment Frameworks Can Improve LLM Alignment

Karolina Stanczak

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 Marasovic

Sylvie Delacroix

Gillian K. Hadfield

Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values… (voir plus) - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.

2025-03-04

Bi-Align @ International Conference on Learning Representations (poster)

Large language models deconstruct the clinical intuition behind diagnosing autism

Emmett Rabot

Laurent Mottron

2025-02-28

Cell (publié)

BigDocs: An Open Dataset for Training Multi-modal Models on Document and Code Tasks

Juan Rodriguez

Xiangru Jian

Siba Smarak Panigrahi

Akshay Kalkunte

Amirhossein Abaskohi

Pierre-Andre Noel

Sanket Biswas … (voir 23 de plus)

Sara Shanian

Ying Zhang

Noah Bolger

Kurt MacDonald

Simon Fauvel

Sathwik Tejaswi

Srinivas Sunkara

Joao Monteiro

Krishnamurthy Dj Dvijotham

Torsten Scholak

Nicolas Chapados

Sepideh Kharagani

Sean Hughes

M. Özsu

Christopher Pal

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 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 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 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 showed a preference for outputs from models trained on BigDocs over 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. The project is hosted at https://bigdocs.github.io .

2025-01-21

International Conference on Learning Representations (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 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 Šuppa

Crystina Zhang

Roberta Rocca

Mohammed Hamdy

Andrianos Michail

John Yang

Manuel Faysse

Aleksei Vatolin

Nandan Thakur

Manan Dey

Dipam Vasani

Pranjal Chitale

Simone Tedeschi

Nguyen Tai

Artem Snegirev

Michael Günther

Mengzhou Xia

Weijia Shi

Xing Han Lu

Jordan Clive

Gayatri Krishnakumar

Anna Maksimova

Silvan Wehrli

Maria Tikhonova

Henil Panchal

Aleksandr Abramov

Malte Ostendorff

Zheng Liu

Simon Clematide

Lester James 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.

2025-01-21

ICLR.cc/2025/Conference (poster)

Exploiting Instruction-Following Retrievers for Malicious Information Retrieval

Parishad BehnamGhader

Nicholas Meade

Instruction-following retrievers have been widely adopted alongside LLMs in real-world applications, but little work has investigated the sa… (voir plus)fety risks surrounding their increasing search capabilities. We empirically study the ability of retrievers to satisfy malicious queries, both when used directly and when used in a retrieval augmented generation-based setup. Concretely, we investigate six leading retrievers, including NV-Embed and LLM2Vec, and find that given malicious requests, most retrievers can (for >50% of queries) select relevant harmful passages. For example, LLM2Vec correctly selects passages for 61.35% of our malicious queries. We further uncover an emerging risk with instruction-following retrievers, where highly relevant harmful information can be surfaced by exploiting their instruction-following capabilities. Finally, we show that even safety-aligned LLMs, such as Llama3, can satisfy malicious requests when provided with harmful retrieved passages in-context. In summary, our findings underscore the malicious misuse risks associated with increasing retriever capability.

2024-12-31

ACL (Findings) (publié)

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Le Zhang

Bo Wang

Xipeng Qiu

Aishwarya Agrawal

We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, sign… (voir plus)ificantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular information retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results underscore the effectiveness of our approach and highlight how reinforcement learning can enhance LLM reasoning capabilities in reranking.

2024-12-31

EMNLP (publié)

Retreever: Tree-Based Coarse-to-Fine Representations for Retrieval

Tianyi Chen

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.

2024-12-31

arXiv (prépublication)

Thibault Le Sellier De Chezelles

The BrowserGym Ecosystem for Web Agent Research

Maxime Gasse

Alexandre Lacoste

Massimo Caccia

Lawrence Keunho Jang

Ori Yoran

Dehan Kong

Frank F. Xu

Quentin Cappart

Graham Neubig

Ruslan Salakhutdinov

Nicolas Chapados

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs). Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. In an earlier work, Drouin et al. (2024) introduced BrowserGym which aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature and includes AgentLab, a complementary framework that aids in agent creation, testing, and analysis. Our proposed ecosystem offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. 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 6 popular web agent benchmarks made 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.

2024-12-31

Trans. Mach. Learn. Res. (publié)

The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning

Milad Aghajohari

Kamran Chitsaz

Amirhossein Kazemnejad

Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Y… (voir plus)et the standard RL"thinking environment", where the state is the prompt plus all prior reasoning tokens, makes the state unbounded and forces attention-based policies to pay quadratic compute as thoughts lengthen. We revisit the environment itself. We propose Markovian Thinking, a paradigm in which the policy advances reasoning while conditioning on a constant-size state, decoupling thinking length from context size. As an immediate consequence this yields linear compute with constant memory. We instantiate this idea with Delethink, an RL environment that structures reasoning into fixed-size chunks. Within each chunk, the model thinks as usual; at the boundary, the environment resets the context and reinitializes the prompt with a short carryover. Through RL, the policy learns to write a textual state near the end of each chunk sufficient for seamless continuation of reasoning after reset. Trained in this environment, an R1-Distill 1.5B model reasons in 8K-token chunks yet thinks up to 24K tokens, matching or surpassing LongCoT-RL trained with a 24K budget. With test-time scaling, Delethink continues to improve where LongCoT plateaus. The effect of linear compute is substantial: we empirically estimate at 96K average thinking length LongCoT-RL costs 27 H100-months vs. 7 for Delethink. Analysis at RL initialization shows off-the-shelf reasoning models (1.5B-120B) often sample Markovian traces zero-shot across diverse benchmarks, providing positive samples that make RL effective at scale. Our results show that redesigning the thinking environment is a powerful lever: it enables very long reasoning without quadratic overhead and opens a path toward efficient, scalable reasoning LLMs.

2024-12-31

arXiv (prépublication)

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

Senyu Li

Zipeng Sun

Jiayi Wang

Xue Liu

Pontus Stenetorp

David Ifeoluwa Adelani

Traditional supervised fine-tuning (SFT) strategies for sequence-to-sequence tasks often train models to directly generate the target output… (voir plus). Recent work has shown that guiding models with intermediate steps, such as keywords, outlines, or reasoning chains, can significantly improve performance, coherence, and interpretability. However, these methods often depend on predefined intermediate formats and annotated data, limiting their scalability and generalizability. In this work, we introduce a task-agnostic framework that enables models to generate intermediate "warmup" sequences. These warmup sequences, serving as an initial state for subsequent generation, are optimized to enhance the probability of generating the target sequence without relying on external supervision or human-designed structures. Drawing inspiration from reinforcement learning principles, our method iteratively refines these intermediate steps to maximize their contribution to the final output, similar to reward-driven optimization in reinforcement learning with human feedback. Experimental results across tasks such as translation, summarization, and multi-choice question answering for logical reasoning show that our approach outperforms traditional SFT methods, and offers a scalable and flexible solution for sequence-to-sequence tasks.

2024-12-31

ACL (1) (publié)