24 citations · 36 across the 10 of their papers we have counts for
10 papers
When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation
Pranav Rakasi, Maanas Lalwani, Arnav Srivastava +4
Large language models for code generation often produce incorrect solutions without reliable indicators of failure. We study whether uncertainty estimation methods developed for na…
BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents
Jinge Wu, Hongjian Zhou, Mingde Zeng +8
Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness an…
Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
Hongjian Zhou, Xinyu Zou, Jinge Wu +19
Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increa…
Scientific reasoning does not reliably translate into scientific forecasting in frontier AI
Sean Wu, Pan Lu, Yupeng Chen +7
AI systems are increasingly used to support forward-looking scientific judgment, but it remains unclear whether they can form reliable expectations about future scientific advances…
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3
Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response a…
Entropy Alone is Insufficient for Safe Selective Prediction in LLMs
Edward Phillips, Fredrik K. Gustafsson, Sean Wu +2
Selective prediction systems can mitigate harms resulting from language model hallucinations by abstaining from answering in high-risk cases. Uncertainty quantification techniques…
AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients
Sean Wu, Michael Koo, Fabien Scalzo +1
Large language models (LLMs) have demonstrated increasingly sophisticated performance in medical and other fields of knowledge. Traditional methods of creating specialist LLMs requ…
Adversarial Databases Improve Success in Retrieval-based Large Language Models
Sean Wu, Michael Koo, Li Yo Kao +4
Open-source LLMs have shown great potential as fine-tuned chatbots, and demonstrate robust abilities in reasoning and surpass many existing benchmarks. Retrieval-Augmented Generati…
A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology
Sean Wu, Michael Koo, Lesley Blum +4
In recent years, there have been significant breakthroughs in the field of natural language processing, particularly with the development of large language models (LLMs). These LLM…
aUToLights: A Robust Multi-Camera Traffic Light Detection and Tracking System
Sean Wu, Nicole Amenta, Jiachen Zhou +2
Following four successful years in the SAE AutoDrive Challenge Series I, the University of Toronto is participating in the Series II competition to develop a Level 4 autonomous pas…