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

Reihaneh Rabbany

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. This requires policymakers, industry, researchers and the broader public to collectively work toward securing positive outcomes from AI’s development. AI safety research is a key dimension. Given that the state of science today for building trustworthy AI does not fully cover all risks, accelerated investment in research is required to keep pace with commercially driven growth in system capabilities. Goals: The 2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety aims to support research in this important space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. The result, The Singapore Consensus on Global AI Safety Research Priorities, builds on the International AI Safety Report-A (IAISR) chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this document 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). Through the Singapore Consensus, we hope to globally facilitate meaningful conversations between AI scientists and AI policymakers for maximally beneficial outcomes. Our goal is to enable more impactful R&D efforts to rapidly develop safety and evaluation mechanisms and foster a trusted ecosystem where AI is harnessed for the public good.

2025-06-25

ArXiv (prépublication)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Jarrid Rector-Brooks

Mohsin Hasan

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

2025-01-01

ICLR (publié)

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

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

Reihaneh Rabbany

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

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

Reihaneh Rabbany

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)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Jarrid Rector-Brooks

Mohsin Hasan

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (voir plus)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.

2024-10-10

ArXiv (prépublication)

FaithDial: A Faithful Benchmark for Information-Seeking Dialogue

Ehsan Kamalloo

Sivan Milton

Osmar Zaiane

Mo Yu

Edoardo Ponti

Siva Reddy

Abstract The goal of information-seeking dialogue is to respond to seeker queries with natural language utterances that are grounded on know… (voir plus)ledge sources. However, dialogue systems often produce unsupported utterances, a phenomenon known as hallucination. To mitigate this behavior, we adopt a data-centric solution and create FaithDial, a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. We observe that FaithDial is more faithful than WoW while also maintaining engaging conversations. We show that FaithDial can serve as training signal for: i) a hallucination critic, which discriminates whether an utterance is faithful or not, and boosts the performance by 12.8 F1 score on the BEGIN benchmark compared to existing datasets for dialogue coherence; ii) high-quality dialogue generation. We benchmark a series of state-of-the-art models and propose an auxiliary contrastive objective that achieves the highest level of faithfulness and abstractiveness based on several automated metrics. Further, we find that the benefits of FaithDial generalize to zero-shot transfer on other datasets, such as CMU-Dog and TopicalChat. Finally, human evaluation reveals that responses generated by models trained on FaithDial are perceived as more interpretable, cooperative, and engaging.

2022-12-23

Transactions of the Association for Computational Linguistics (publié)

On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

Sivan Milton

Mo Yu

Osmar R Zaiane

Siva Reddy

Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallu… (voir plus)cination. In this work, we investigate the underlying causes of this phenomenon: is hallucination due to the training data, or to the models? We conduct a comprehensive human study on both existing knowledge-grounded conversational benchmarks and several state-of-the-art models. Our study reveals that the standard benchmarks consist of > 60% hallucinated responses, leading to models that not only hallucinate but even amplify hallucinations. Our findings raise important questions on the quality of existing datasets and models trained using them. We make our annotations publicly available for future research.

2022-04-17

ArXiv (preprint)

On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

Sivan Milton

Mo Yu

Osmar R Zaiane

Siva Reddy

Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallu… (voir plus)cination. In this work, we investigate the underlying causes of this phenomenon: is hallucination due to the training data, or to the models? We conduct a comprehensive human study on both existing knowledge-grounded conversational benchmarks and several state-of-the-art models. Our study reveals that the standard benchmarks consist of > 60% hallucinated responses, leading to models that not only hallucinate but even amplify hallucinations. Our findings raise important questions on the quality of existing datasets and models trained using them. We make our annotations publicly available for future research.

2022-04-17

ArXiv (prépublication)