3 citations · 9 across the 24 of their papers we have counts for
6 papers · 1 filter
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
Yifan Yang, Qiao Jin, Robert Leaman +15
The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with…
Structural Causality-based Generalizable Concept Discovery Models
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
The rising need for explainable deep neural network architectures has utilized semantic concepts as explainable units. Several approaches utilizing disentangled representation lear…
ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
Guangzhi Xiong, Sanchit Sinha, Aidong Zhang
Generalized additive models (GAMs) have long been a powerful white-box tool for the intelligible analysis of tabular data, revealing the influence of each feature on the model pred…
Entry-level guide to the use of large language models for medical research
Qiao Jin, Nicholas Wan, Robert Leaman +20
Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing variou…
CoLiDR: Concept Learning using Aggregated Disentangled Representations
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
Interpretability of Deep Neural Networks using concept-based models offers a promising way to explain model behavior through human-understandable concepts. A parallel line of resea…
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
Sanchit Sinha, Yuguang Yue, Victor Soto +3
Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts…