Siva Reddy

vaibhav.adlakha@mila.quebec

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

parishad.behnamghader@mila.quebec

PhD - McGill University

mehar.bhatia@mila.quebec

matteo.boglioni@mila.quebec

Matteo Boglioni

Collaborating researcher - McGill University

verna.dankers@mila.quebec

Jerry Chen

Research Intern - McGill University

Postdoctorate - McGill University

Independent visiting researcher

Co-supervisor :

Yoshua Bengio

desmond.elliott@mila.quebec

spandana.gella@mila.quebec

Jay Gala

Master's Research - McGill University

Co-supervisor :

Collaborating researcher

gaurav.kamath@mila.quebec

Gaurav Kamath

PhD - McGill University

Co-supervisor :

Timothy O'Donnell

Aditi Khandelwal

PhD - McGill University

Principal supervisor :

Golnoosh Farnadi

aditi.khandelwal@mila.quebec

Austin Kraft

PhD - McGill University

Co-supervisor :

Timothy O'Donnell

austin.kraft@mila.quebec

Benno Krojer

PhD - McGill University

benno.krojer@mila.quebec

Zichao Li

PhD - McGill University

Co-supervisor :

Jackie Cheung

zichao.li@mila.quebec

Fengyuan Liu

Master's Research - McGill University

Co-supervisor :

Dzmitry Bahdanau

fengyuan.liu@mila.quebec

sreenath.madathil@mila.quebec

PhD - McGill University

Master's Research - McGill University

nicholas.meade@mila.quebec

Nicholas Meade

PhD - McGill University

Postdoctorate - McGill University

marius.mosbach@mila.quebec

Marzia Nouri

Master's Research - McGill University

marzia.nouri@mila.quebec

Arkil Patel

PhD - McGill University

Principal supervisor :

Dzmitry Bahdanau

arkil.patel@mila.quebec

erfan.raoofian@mila.quebec

Neel Rajani

Collaborating researcher - University of Edinburgh

neel.rajani@mila.quebec

Collaborating researcher - N/A

Ben Saine

Collaborating researcher - McGill University

ben.saine@mila.quebec

dongchan.shin@mila.quebec

Dongchan Shin

Collaborating Alumni

tomasvergara.browne@mila.quebec

Ada Tur

Research Intern - McGill University

PhD - McGill University

Collaborating researcher - McGill University

donghao.zeng@mila.quebec

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

Blog Posts

October 1, 2024

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning

Thibault Rousset

LLMs memorize sensitive training data, including personally identifiable information (PII), creating a pressing need for reliable post hoc r… (see more)emoval methods. Unlearning has emerged as a promising solution, with state-of-the-art(SOTA) methods often following a localize-first, unlearn-second paradigm that targets specific model parameters. However, existing benchmarks evaluate unlearning solely at the output level, leaving open the question of whether unlearning truly erases knowledge from a model's parameters or merely obfuscates it, a concern reinforced by the success of resurfacing attacks. To bridge this gap, we introduce LACUNA: the first unlearning testbed with ground-truth parameter-level localization. LACUNA injects PII of synthetic individuals into predefined parameters of 1B and 7B OLMo-based models via masked continual pretraining, enabling direct evaluation of whether unlearning targets the weights responsible for knowledge storage. We use LACUNA to benchmark current SOTA unlearning methods and find that, despite strong output-level performance, existing methods are highly imprecise and susceptible to resurfacing attacks. We further show that when localization is successful, even a simple gradient-based unlearning method achieves strong erasure and robustness to resurfacing attacks, highlighting the importance of precise unlearning. We release LACUNA to complement behavioral evaluations and drive further advances in robust, localization-based unlearning.

2026-07-01

arXiv (preprint)

Societal Frameworks Can Improve LLM Alignment

Karolina Stanczak

Nicholas Meade

Mehar Bhatia

Hattie Zhou

Konstantin Böttinger

Jeremy Barnes

Jason Stanley

Nicolas Papernot

Nicolas Chapados

Denis Therien

Timothy 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… (see more) — 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. It is this pervasive uncertainty that necessitates our alternative view on LLM alignment, framing the under-specified 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.

2026-06-24

ACM Conference on Fairness, Accountability, and Transparency (published)

Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation

Mixture-of-Experts (MoE) models are widely used to scale language models, yet their expert routing behavior and adaptation in a multilingual… (see more) setting remain underexplored. In this work, we study multilingual routing dynamics during continual pre-training of an English-centric MoE model on a multilingual corpus, analyzing how expert usage varies across languages. We find that continual multilingual pre-training leads to diffused, language-agnostic routing in early and middle layers, with language specialization primarily emerging in the final layers. We also show that token-level vocabulary overlap between languages plays an important role in how languages are routed. Motivated by these findings, we propose a parameter-efficient adaptation strategy that updates language-specific and shared experts in the final MoE layers. Experiments on MultiBLiMP and Belebele show that our method achieves a strong performance-efficiency trade-off, attaining competitive performance relative to fine-tuning complete final layers, while updating less than 2% of the parameters. Overall, our findings provide insights into where and how language specialization emerges in MoEs during continual pre-training and provide practical insights for low-resource multilingual adaptation. Our code is available at https://github.com/aditi184/moe-routing-adaptation.

2026-05-27

arXiv (preprint)

WebArena-Pro: A Heterogeneous, Multimodal, Reproducible Benchmark for Web Agents

Imene Kerboua

Fatemeh Pesaran zadeh

Weijian Qi

Alexander Miller

Junyi Song

Yunjia Tian

Dongjin Kang

Seyeon Choi

Marzia Nouri

Ewen Gueguen

Matteo Boglioni

Fengyuan Liu

Zeyi Liao

Mengqi Yuan

Yue Li

Alexandre Lacoste

Alexandre Drouin

Spandana Gella

Huan Sun … (see 2 more)

Gunhee Kim

Web agents powered by large language and vision-language models are increasingly applied to realistic browser work that spans heterogeneous … (see more)applications, multimodal content, and stateful workflows. However, existing reproducible web-agent benchmarks cover only a small number of web applications drawn from a few software categories, and restrict modality to text and vision. Live benchmarks broaden site coverage but sacrifice reproducibility, since pages and data drift between runs. Moreover, existing benchmarks do not meaningfully evaluate whether agents can understand and use audio and video content embedded within web tasks. To address these gaps, we introduce WebArena-Pro, a benchmark comprising 300 tasks across 20 self-hosted web applications in six domain categories, spanning distinct interface conventions, workflows, and data models. Across the evaluated agents, the best performance is achieved by Gemini 3.1 Pro, which attains 37.0 % success under a 50-step budget, while open-source models' performance does not exceed 27.7% success. Among reproducible, human-curated web agent benchmarks, WebArena-Pro provides the broadest application coverage and the most comprehensive multimodal support to date. The benchmark treats audio and video as core observations alongside text and vision, with dedicated actions for extracting information from each. WebArena-Pro runs each task in isolation and supports reproducible, parallel evaluation. Tasks are authored through a dedicated annotator interface, filtered by LLM-assisted triage, and finally validated by humans before release.

2026-05-22

AIWILD @ International Conference on Machine Learning (published)

CUBE: A Standard for Unifying Agent Benchmarks

Alexandre Lacoste

Nicolas Gontier

Oleh Shliazhko

Aman Jaiswal

Kusha Sareen

Shailesh Nanisetty

Joan Cabezas

Manuel Del Verme

Omar G. Younis

Simone Baratta

Matteo Avalle

Imene Kerboua

Elron Bandel

Michal Shmueli-Scheuer

Asaf Yehudai

Leshem Choshen

Jonathan Lebensold

Sean Hughes

Massimo Caccia … (see 6 more)

Alexandre Drouin

Tao Yu

Yu Su

Graham Neubig

Dawn Song

The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires s… (see more)ubstantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform-specific implementations deepen fragmentation as benchmark production accelerates through 2026.

2026-03-15

arXiv (preprint)

LLM2Vec-Gen: Generative Embeddings from Large Language Models

Parishad BehnamGhader

Fabian David Schmidt

LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to si… (see more)milar outputs. Typically, this input-output is addressed by training embedding models with paired data using contrastive learning. In this work, we propose a novel self-supervised approach, LLM2Vec-Gen, which adopts a different paradigm: rather than encoding the input, we learn to represent the model's potential response. Specifically, we add trainable special tokens to the LLM's vocabulary, append them to input, and optimize them to represent the LLM's response in a fixed-length sequence. Training is guided by the LLM's own completion for the query, along with an unsupervised embedding teacher that provides distillation targets. This formulation helps to bridge the input-output gap and transfers LLM capabilities such as safety alignment and reasoning to embedding tasks. Crucially, the LLM backbone remains frozen and training requires only unlabeled queries. LLM2Vec-Gen achieves state-of-the-art self-supervised performance on the Massive Text Embedding Benchmark (MTEB), improving by 9.3% over the best unsupervised embedding teacher. We also observe up to 43.2% reduction in harmful content retrieval and 29.3% improvement in reasoning capabilities for embedding tasks. Finally, the learned embeddings are interpretable and can be decoded into text to reveal their semantic content.

2026-03-10

arXiv (preprint)

Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency

Tianyi Chen

Valentina Zantedeschi

Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets.… (see more) While effective, the standard practice of encoding data into high-dimensional representations for similarity search entails large memory and compute footprints, and also makes it hard to inspect the inner workings of the system. Hierarchical retrieval methods offer an interpretable alternative by organizing data at multiple granular levels, yet do not match the efficiency and performance of flat retrieval approaches. In this paper, we propose ReTreever, a tree-based method that makes hierarchical retrieval viable at scale by directly optimizing its structure for retrieval performance while naturally providing transparency through meaningful semantic groupings. Our method offers the flexibility to balance cost and utility by indexing data using representations from any tree level. We show that ReTreever delivers strong coarse (intermediate levels) and fine representations (terminal level), while achieving the highest retrieval accuracy at the lowest latency among hierarchical methods. These results demonstrate that this family of techniques is viable in practical applications.

2026-03-01

Trustworthy AI @ International Conference on Learning Representations (published)

LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

Transforming a large language model (LLM) into a Vision-Language Model (VLM) can be achieved by mapping the visual tokens from a vision enco… (see more)der into the embedding space of an LLM. Intriguingly, this mapping can be as simple as a shallow MLP transformation. To understand why LLMs can so readily process visual tokens, we need interpretability methods that reveal what is encoded in the visual token representations at every layer of LLM processing. In this work, we introduce LatentLens, a novel approach for mapping latent representations to descriptions in natural language. LatentLens works by encoding a large text corpus and storing contextualized token representations for each token in that corpus. Visual token representations are then compared to their contextualized textual representations, with the top-k nearest neighbor representations providing descriptions of the visual token. We evaluate this method on 10 different VLMs, showing that commonly used methods, such as LogitLens, substantially underestimate the interpretability of visual tokens. With LatentLens instead, the majority of visual tokens are interpretable across all studied models and all layers. Qualitatively, we show that the descriptions produced by LatentLens are semantically meaningful and provide more fine-grained interpretations for humans compared to individual tokens. More broadly, our findings contribute new evidence on the alignment between vision and language representations, opening up new directions for analyzing latent representations.

2026-03-01

Re-Align @ International Conference on Learning Representations (published)

Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection

Fatemeh Pesaran zadeh

Seyeon Choi

Gunhee Kim

Large language models (LLMs) have enabled web agents that follow natural language goals through multi-step browser interactions. However, ag… (see more)ents fine-tuned on specific trajectories and domain often struggle to generalize out of domain, and offline training can be compute-inefficient due to noisy, redundant trajectories and long accessibility-tree (AXTree) states. To address both issues, we propose Weasel, a trajectory selection method for offline training of web agents. Weasel selects a fixed-budget subset of trajectory steps by optimizing an objective that balances unary importance with pairwise diversity over states, websites, and interaction patterns, solving efficiently with a greedy algorithm. We further improve efficiency with action-centered AXTree pruning that keeps only content around the ground-truth action target, and we mitigate style mismatch for reasoning-native models by replacing expert traces with model-generated, style-consistent rationales. Across AgentTrek and NNetNav training datasets, evaluations in WebArena, WorkArena, and MiniWob, and experiments with Qwen2.5-7B, Gemma3-4B, and Qwen3-8B, Weasel improves out-of-domain performance while reducing training cost, producing roughly 9.7-12.5

2026-03-01

LLA @ International Conference on Learning Representations (poster)

Value Drifts: Tracing Value Alignment During LLM Post-Training

Karolina Stanczak

Vered Shwartz

As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to dra… (see more)w on their general knowledge but also to align with certain human value systems. Therefore, studying the alignment of LLMs with human values has become a crucial field of inquiry. Prior work, however, mostly focuses on evaluating the alignment of fully trained models, overlooking the training dynamics by which models learn to express human values. In this work, we investigate how and at which stage value alignment arises during the course of a model's post-training. Our analysis disentangles the effects of post-training algorithms and datasets, measuring both the magnitude and time of value drifts during training. Experimenting with Llama-3 and Qwen-3 models of different sizes and popular supervised fine-tuning (SFT) and preference optimization datasets and algorithms, we find that the SFT phase generally establishes a model's values, and subsequent preference optimization rarely re-aligns these values. Furthermore, using a synthetic preference dataset that enables controlled manipulation of values, we find that different preference optimization algorithms lead to different value alignment outcomes, even when preference data is held constant. Our findings provide actionable insights into how values are learned during post-training and help to inform data curation, as well as the selection of models and algorithms for preference optimization to improve model alignment to human values.

2026-02-27

CAO @ International Conference on Learning Representations (poster)

BRIDGE: Predicting Human Task Completion Time From Model Performance

Nilaksh

Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task diffic… (see more)ulty. Existing approaches that rely on direct human task completion time annotations are costly, noisy, and difficult to scale across benchmarks. In this work, we propose BRIDGE, a unified psychometric framework that learns the latent difficulty scale from model responses and anchors it to human task completion time. Using a two-parameter logistic Item Response Theory model, we jointly estimate latent task difficulty and model capability from model performance data across multiple benchmarks. We demonstrate that latent task difficulty varies linearly with the logarithm of human completion time, allowing human task completion time to be inferred for new benchmarks from model performance alone. Leveraging this alignment, we forecast frontier model capabilities in terms of human task length and independently reproduce METR's exponential scaling results, with the 50% solvable task horizon doubling approximately every 6 months.

2026-02-05

arXiv (preprint)

Operationalizing the Superficial Alignment Hypothesis via Task Complexity

Tiago Pimentel

The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that pos… (see more)t-training merely surfaces this knowledge. The SAH, however, lacks a precise definition, which has led to (i) different and seemingly orthogonal arguments supporting it, and (ii) important critiques to it. We propose a new metric called **Task Complexity**: the length of the shortest program that achieves a target performance on a task. In this framework, the SAH claims that pre-trained models drastically reduce the task complexity of achieving high performance on many tasks. Our definition unifies prior arguments supporting the SAH, interpreting them as different strategies to find such short programs. Experimentally, we estimate task complexities of mathematical reasoning, machine translation, and instruction following tasks and show that their respective task complexities can be remarkably low when conditioned on a pre-trained model. Further, we find that pre-training enables access to strong performances on our tasks, but it can require programs of gigabytes of length to access them. Post-training, on the other hand, collapses the complexity of reaching this same performance by several orders of magnitude. Overall, our results highlight that task adaptation can require remarkably little information—often just a few kilobytes.

2025-12-31

International Conference on Machine Learning (Accept (regular))