Reihaneh Rabbany

PairBench: A Systematic Framework for Selecting Reliable Judge VLMs

Aarash Feizi

Sai Rajeswar

Adriana Romero Soriano

Spandana Gella

Valentina Zantedeschi

Joao Monteiro

As large vision language models (VLMs) are increasingly used as automated evaluators, understanding their ability to effectively compare dat… (voir plus)a pairs as instructed in the prompt becomes essential. To address this, we present PairBench, a low-cost framework that systematically evaluates VLMs as customizable similarity tools across various modalities and scenarios. Through PairBench, we introduce four metrics that represent key desiderata of similarity scores: alignment with human annotations, consistency for data pairs irrespective of their order, smoothness of similarity distributions, and controllability through prompting. Our analysis demonstrates that no model, whether closed- or open-source, is superior on all metrics; the optimal choice depends on an auto evaluator's desired behavior (e.g., a smooth vs. a sharp judge), highlighting risks of widespread adoption of VLMs as evaluators without thorough assessment. For instance, the majority of VLMs struggle with maintaining symmetric similarity scores regardless of order. Additionally, our results show that the performance of VLMs on the metrics in PairBench closely correlates with popular benchmarks, showcasing its predictive power in ranking models.

2025-02-21

ArXiv (prépublication)

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Golnoosh Farnadi

2025-01-01

ACL (1) (publié)

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection

Akshatha Arodi

Ga'etan Marceau Caron

Jean-François Godbout

Deepfakes, synthetic media created using advanced AI techniques, have intensified the spread of misinformation, particularly in politically … (voir plus)sensitive contexts. Existing deepfake detection datasets are often limited, relying on outdated generation methods, low realism, or single-face imagery, restricting the effectiveness for general synthetic image detection. By analyzing social media posts, we identify multiple modalities through which deepfakes propagate misinformation. Furthermore, our human perception study demonstrates that recently developed proprietary models produce synthetic images increasingly indistinguishable from real ones, complicating accurate identification by the general public. Consequently, we present a comprehensive, politically-focused dataset specifically crafted for benchmarking detection against modern generative models. This dataset contains three million real images paired with descriptive captions, which are used for generating 963k corresponding high-quality synthetic images from a mix of proprietary and open-source models. Recognizing the continual evolution of generative techniques, we introduce an innovative crowdsourced adversarial platform, where participants are incentivized to generate and submit challenging synthetic images. This ongoing community-driven initiative ensures that deepfake detection methods remain robust and adaptive, proactively safeguarding public discourse from sophisticated misinformation threats.

2025-01-01

arXiv.org (prépublication)

PairBench: A Systematic Framework for Selecting Reliable Judge VLMs

Aarash Feizi

Sai Rajeswar

Adriana Romero Soriano

Spandana Gella

Valentina Zantedeschi

Joao Monteiro

As large vision language models (VLMs) are increasingly used as automated evaluators, understanding their ability to effectively compare dat… (voir plus)a pairs as instructed in the prompt becomes essential. To address this, we present PairBench, a low-cost framework that systematically evaluates VLMs as customizable similarity tools across various modalities and scenarios. Through PairBench, we introduce four metrics that represent key desiderata of similarity scores: alignment with human annotations, consistency for data pairs irrespective of their order, smoothness of similarity distributions, and controllability through prompting. Our analysis demonstrates that no model, whether closed- or open-source, is superior on all metrics; the optimal choice depends on an auto evaluator's desired behavior (e.g., a smooth vs. a sharp judge), highlighting risks of widespread adoption of VLMs as evaluators without thorough assessment. For instance, the majority of VLMs struggle with maintaining symmetric similarity scores regardless of order. Additionally, our results show that the performance of VLMs on the metrics in PairBench closely correlates with popular benchmarks, showcasing its predictive power in ranking models.

2025-01-01

arXiv.org (prépublication)

The Singapore Consensus on Global AI Safety Research Priorities

Yoshua Bengio

Tegan Maharaj

Luke Ong

Stuart Russell

Dawn Song

Max Tegmark

Lan Xue

Ya-Qin Zhang

Stephen Casper

Wan Sie Lee

Sören Mindermann

Vanessa Wilfred

Vidhisha Balachandran

Fazl Barez

Michael Belinsky

Imane Bello

Malo Bourgon

Mark Brakel

Sim'eon Campos

Duncan Cass-Beggs … (voir 67 de plus)

Jiahao Chen

Rumman Chowdhury

Kuan Chua Seah

Jeff Clune

Juntao Dai

Agnès Delaborde

Nouha Dziri

Francisco Eiras

Joshua Engels

Jinyu Fan

Adam Gleave

Noah D. Goodman

Fynn Heide

Johannes Heidecke

Dan Hendrycks

Cyrus Hodes

Bryan Low Kian Hsiang

Minlie Huang

Sami Jawhar

Jingyu Wang

Adam Tauman Kalai

Meindert Kamphuis

Mohan S. Kankanhalli

Subhash Kantamneni

Mathias Bonde Kirk

Thomas Kwa

Jeffrey Ladish

Kwok-Yan Lam

Wan Lee Sie

Taewhi Lee

Xiaojian Li

Jiajun Liu

Chaochao Lu

Yifan Mai

Richard Mallah

Julian Michael

Nick Moës

Simon Möller

Kihyuk Nam

Kwan Yee Ng

Mark Nitzberg

Besmira Nushi

Sean O hEigeartaigh

Alejandro Ortega

Pierre Peigné

James Petrie

Benjamin Prud'homme

Nayat Sanchez-Pi

Sarah Schwettmann

Buck Shlegeris

Saad Siddiqui

Aradhana Sinha

Martín Soto

Cheston Tan

Dong Ting

William-Chandra Tjhi

Robert Trager

Brian Tse

H. AnthonyTungK.

John Willes

Denise Wong

W. Xu

Rongwu Xu

Yi Zeng

HongJiang Zhang

Djordje Zikelic

Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (voir plus)sure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. This resulting report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this report organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control).

2025-01-01

arXiv.org (prépublication)

The Singapore Consensus on Global AI Safety Research Priorities

Yoshua Bengio

Tegan Maharaj

Luke Ong

Stuart Russell

Dawn Song

Max Tegmark

Lan Xue

Ya-Qin Zhang

Stephen Casper

Wan Sie Lee

Sören Mindermann

Vanessa Wilfred

Vidhisha Balachandran

Fazl Barez

Michael Belinsky

Imane Bello

Malo Bourgon

Mark Brakel

Sim'eon Campos

Duncan Cass-Beggs … (voir 67 de plus)

Jiahao Chen

Rumman Chowdhury

Kuan Chua Seah

Jeff Clune

Juntao Dai

Agnès Delaborde

Nouha Dziri

Francisco Eiras

Joshua Engels

Jinyu Fan

Adam Gleave

Noah D. Goodman

Fynn Heide

Johannes Heidecke

Dan Hendrycks

Cyrus Hodes

Bryan Low Kian Hsiang

Minlie Huang

Sami Jawhar

Jingyu Wang

Adam Tauman Kalai

Meindert Kamphuis

Mohan S. Kankanhalli

Subhash Kantamneni

Mathias Bonde Kirk

Thomas Kwa

Jeffrey Ladish

Kwok-Yan Lam

Wan Lee Sie

Taewhi Lee

Xiaojian Li

Jiajun Liu

Chaochao Lu

Yifan Mai

Richard Mallah

Julian Michael

Nick Moës

Simon Möller

Kihyuk Nam

Kwan Yee Ng

Mark Nitzberg

Besmira Nushi

Sean O hEigeartaigh

Alejandro Ortega

Pierre Peigné

James Petrie

Benjamin Prud'homme

Nayat Sanchez-Pi

Sarah Schwettmann

Buck Shlegeris

Saad Siddiqui

Aradhana Sinha

Martín Soto

Cheston Tan

Dong Ting

William Tjhi

Robert Trager

Brian Tse

H. AnthonyTungK.

John Willes

Denise Wong

Wei Xu

Rongwu Xu

Yi Zeng 0005

HongJiang Zhang

Djordje Zikelic

2025-01-01

arXiv.org (prépublication)

Online Influence Campaigns: Strategies and Vulnerabilities

Andreea Musulan

Veronica Xia

Ethan Kosak-Hine

Tom Gibbs

Vidya Sujaya

Jean-François Godbout

Kellin Pelrine

U. Montr'eal

Ivado

M. University

In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, soci… (voir plus)etal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target. We also provide an overview of three case studies of extensive manipulation campaigns to emphasize the severity of the problem. We then address the role that Artificial Intelligence plays in the development and dissemination of harmful content, and how its evolution presents new threats to societal cohesion for countries across the globe. Our survey aims to increase our understanding of not just particular aspects of these threats, but also the strategies underlying their deployment, so we can effectively prepare for the evolving cybersecurity landscape.

2024-12-18

ArXiv (prépublication)

Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data

In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel … (voir plus)architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets.

2024-12-13

ArXiv (prépublication)

Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data

In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel … (voir plus)architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets.

2024-12-13

ArXiv (prépublication)

Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data

In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel … (voir plus)architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets.

2024-12-13

ArXiv (prépublication)

Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data

Transformers are now ubiquitous for sequence modeling tasks, but their extension to multi-dimensional data remains a challenge due to the qu… (voir plus)adratic cost of the attention mechanism. In this paper, we propose Higher-Order Transformers (HOT), a novel architecture designed to efficiently process data with more than two axes, i.e. higher-order tensors. To address the computational challenges associated with high-order tensor attention, we introduce a novel Kronecker factorized attention mechanism that reduces the attention cost to quadratic in each axis' dimension, rather than quadratic in the total size of the input tensor. To further enhance efficiency, HOT leverages kernelized attention, reducing the complexity to linear. This strategy maintains the model's expressiveness while enabling scalable attention computation. We validate the effectiveness of HOT on two high-dimensional tasks, including multivariate time series forecasting, and 3D medical image classification. Experimental results demonstrate that HOT achieves competitive performance while significantly improving computational efficiency, showcasing its potential for tackling a wide range of complex, multi-dimensional data.

2024-12-04

ArXiv (prépublication)