1k citations · 2.6k across the 149 of their papers we have counts for
15 papers · 1 filter
CARE: A Conformal Safety Layer for Medical Summarization
Suhana Bedi, Bridget Lin, Anson Y. Zhou +5
Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing…
Reasoning Models Don't Just Think Longer, They Move Differently
Anders Gjølbye, Lars Kai Hansen, Sanmi Koyejo
Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computing for more steps or followi…
Why Do Safety Guardrails Degrade Across Languages?
Max Zhang, Ameen Patel, Sang T. Truong +1
Large language models exhibit safety degradation in non-English languages. Standard evaluation relies on Jailbreak Success Rate (JSR), which confounds several safety-driving factor…
TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
Fangrui Huang, Souhad Chbeir, Arpandeep Khatua +8
Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks-…
Extracting books from production language models
Ahmed Ahmed, A. Feder Cooper, Sanmi Koyejo +1
Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's weights during training, and whether…
CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs
Truong Vo, Sanmi Koyejo
Large language models (LLMs) are increasingly deployed in culturally diverse environments, yet existing evaluations of cultural competence remain limited. Existing methods focus on…
SpecEval: Evaluating Model Adherence to Behavior Specifications
Ahmed Ahmed, Kevin Klyman, Yi Zeng +2
Companies that develop foundation models publish behavioral guidelines they pledge their models will follow, but it remains unclear if models actually do so. While providers such a…
The Sound of Syntax: Finetuning and Comprehensive Evaluation of Language Models for Speech Pathology
Fagun Patel, Duc Q. Nguyen, Sang T. Truong +3
According to the U.S. National Institutes of Health, more than 3.4 million children experience speech disorders that require clinical intervention. The number of speech-language pa…
The Inadequacy of Offline LLM Evaluations: A Need to Account for Personalization in Model Behavior
Angelina Wang, Daniel E. Ho, Sanmi Koyejo
Standard offline evaluations for language models -- a series of independent, state-less inferences made by models -- fail to capture how language models actually behave in practice…
Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs
Aryan Gulati, Brando Miranda, Eric Chen +5
Current mathematical reasoning benchmarks for large language models (LLMs) are approaching saturation, with some achieving > 90% accuracy, and are increasingly compromised by train…
UQ: Assessing Language Models on Unsolved Questions
Fan Nie, Ken Ziyu Liu, Zihao Wang +11
Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usa…
Reliable and Efficient Amortized Model-based Evaluation
Sang Truong, Yuheng Tu, Percy Liang +2
Comprehensive evaluations of language models (LM) during both development and deployment phases are necessary because these models possess numerous capabilities (e.g., mathematical…
Best-of-N Jailbreaking
John Hughes, Sara Price, Aengus Lynch +7
We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variati…
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models
Sang T. Truong, Duc Q. Nguyen, Toan Nguyen +4
Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretraining on multili…
Transforming and Combining Rewards for Aligning Large Language Models
Zihao Wang, Chirag Nagpal, Jonathan Berant +4
A common approach for aligning language models to human preferences is to first learn a reward model from preference data, and then use this reward model to update the language mod…