44 citations · 62 across the 11 of their papers we have counts for
14 papers
International AI Safety Report 2026
Yoshua Bengio, Stephen Clare, Carina Prunkl +89
The International AI Safety Report 2026 synthesises the current scientific evidence on the capabilities, emerging risks, and safety of general-purpose AI systems. The report series…
International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications
Yoshua Bengio, Stephen Clare, Carina Prunkl +70
Since the publication of the first International AI Safety Report, AI capabilities have continued to improve across key domains. New training techniques that teach AI systems to re…
SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
Tiancheng Hu, Joachim Baumann, Lorenzo Lupo +3
Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human b…
Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
Tiancheng Hu, Benjamin Minixhofer, Nigel Collier
The "alignment tax" of post-training is typically framed as a drop in task accuracy. We show it also involves a severe loss of calibration, making models overconfident, less reliab…
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News
Tiancheng Hu, Nigel Collier
Understanding how individuals perceive and react to information is fundamental for advancing social and behavioral sciences and developing human-centered AI systems. Current approa…
When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
Yijiang River Dong, Tiancheng Hu, Yinhong Liu +2
While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences ac…
Can LLM be a Personalized Judge?
Yijiang River Dong, Tiancheng Hu, Nigel Collier
Ensuring that large language models (LLMs) reflect diverse user values and preferences is crucial as their user bases expand globally. It is therefore encouraging to see the growin…
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models
Bolei Ma, Xinpeng Wang, Tiancheng Hu +4
Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. T…
Can Language Models Recognize Convincing Arguments?
Paula Rescala, Manoel Horta Ribeiro, Tiancheng Hu +1
The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives. Here, we study their performance in detec…
Quantifying the Persona Effect in LLM Simulations
Tiancheng Hu, Nigel Collier
Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables-demographic, social,…
Generative Language Models Exhibit Social Identity Biases
Tiancheng Hu, Yara Kyrychenko, Steve Rathje +3
The surge in popularity of large language models has given rise to concerns about biases that these models could learn from humans. We investigate whether ingroup solidarity and ou…
Quotatives Indicate Decline in Objectivity in U.S. Political News
Tiancheng Hu, Manoel Horta Ribeiro, Robert West +1
According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as "said" and "told", as nonobjective quotatives, like "argued" an…
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News
Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli +7
Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality…
The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention
Sumit Jha, Mohamed F. Marzban, Tiancheng Hu +3
A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learni…