1 citations · 1 across the 13 of their papers we have counts for
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FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect
Hazel H. Kim, Andrew M. Bean, Guilherme Affonso Ferreira de Camargo +10
We introduce FramingQA, a benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations. Large language models (LLMs) oft…
Thomson: Continual Learning of Frontier Models for SovereignAI
Shengzhuang Chen, Jerrod Parker, Yejin Bang +23
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetr…
InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries
Samuel J. Vincent, Daniel Calloway, Fangyi Yu +2
Legal AI systems are increasingly used to answer legal questions, yet existing benchmarks assume queries arrive fully specified. In practice, users omit facts that materially deter…
ContractScrub: A benchmark for final review of legal contracts
Yejin Bang, Kirsty Fielding, Brandan Oliver +3
Legal work, with its heavy reliance on processing large amounts of text, is often considered one of the domains most exposed to the use of LLMs. Contract ``scrubbing,'' the final r…
How sensitive do we want AI to be? Socio-communicative competencies of large language models in healthcare
Dorothee Amelung, Andrew M. Bean, Sabine C. Herpertz +4
Background. Effective clinical practice relies heavily on the socio-communicative skills of medical professionals. Large language models (LLMs) have been proposed for tasks such as…
Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results
Jan Batzner, Sree Harsha Nelaturu, Damian Stachura +45
AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
To Whom Do Language Models Align? Measuring Principal Hierarchies Under High-Stakes Competing Demands
Fangyi Yu, Nabeel Seedat, Jonathan Richard Schwarz +1
Language models deployed in high-stakes professional settings face conflicting demands from users, institutional authorities, and professional norms. How models act when these dema…