Nicolas Chapados

Étienne Marcotte

Valentina Zantedeschi

Alexandre Drouin

Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (voir plus)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via na\"ive direct prompting, their full potential remains underexplored. We address this gap with 4 strategies, providing new insights into the zero-shot capabilities of LLMs in this setting. ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context independently from its forecast accuracy. CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines. IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models. Finally, RouteDP optimizes resource efficiency by using LLMs to estimate task difficulty, and routing the most challenging tasks to larger models. Evaluated on different kinds of context-aided forecasting tasks from the CiK benchmark, our strategies demonstrate distinct benefits over na\"ive prompting across LLMs of different sizes and families. These results open the door to further simple yet effective improvements in LLM-based context-aided forecasting.

2025-08-13

ArXiv (prépublication)

arxiv.org

Spaced Scheduling for Large Language Model Training

Amine El hattami

Chris Pal

2025-06-09

TMLR (accepté)

Silent Sabotage: Injecting Backdoors into AI Agents Through Fine-Tuning

Léo Boisvert

Abhay Puri

Chandra Kiran Reddy Evuru

Joshua Kazdan

Avinandan Bose

Quentin Cappart

Maryam Fazel

Sai Rajeswar

Jason Stanley

Alexandre Drouin

Krishnamurthy Dj Dvijotham

The rise of AI agents that can use tools, browse the web and interact with computers on behalf of a user, has sparked strong interest in imp… (voir plus)roving these capabilities by explicitly fine-tuning the LLMs/VLMs that power these agents. Several researchers have proposed collecting data by letting the agents interact with their environment (e.g., a computer operating system, the web or a collection of APIs exposed as tools), and improve agent performance by fine tuning on this data. In this work, we show that such data collection can be manipulated by adversaries to insert poisoned traces. By modifying just 5% of collected traces, adversaries can embed stealthy bad behaviors into agents—like leaking confidential user information whenever the tool or webpage exposes a trigger. Our results raise important security concerns in the development of AI agents, and underscore the importance of careful scrutiny of all data collection processes used to improve agentic AI.

2025-06-08

ICML.cc/2025/Workshop/WCUA (poster)

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Andrew Robert Williams

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Alexandre Lacoste

2025-05-01

ICML.cc/2025/Conference (poster)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

M. Tamer Özsu

David Vazquez

Perouz Taslakian

Sai Rajeswar

Human Annotator

2025-05-01

ICML.cc/2025/Conference (poster)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

M. T. ¨Ozsu

David Vazquez

Perouz Taslakian

Sai Rajeswar

Human Annotator

2025-03-19

ArXiv (prépublication)

arxiv.org

AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding

Juan A. Rodriguez

Chao Wang

Akshay Kalkunte Suresh

Abhay Puri

Xiangru Jian

Pierre-Andre Noel

Sathwik Tejaswi Madhusudhan

Enamul Hoque

Issam Hadj Laradji

David Vazquez

Perouz Taslakian … (voir 2 de plus)

Spandana Gella

Sai Rajeswar

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges… (voir plus) on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.

2025-03-05

ICLR.cc/2025/Workshop/Re-Align (poster)

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

Denis Therien

Timothy P. Lillicrap

Ana Marasovic

Sylvie Delacroix

Gillian K. Hadfield

Siva Reddy

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.

2025-03-05

ICLR.cc/2025/Workshop/Bi-Align (poster)

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

Denis Therien

Timothy P. Lillicrap

Ana Marasovi'c

Sylvie Delacroix

Gillian K. Hadfield

Siva Reddy

2025-02-27

ArXiv (prépublication)

arxiv.org

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

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)