5 papers
Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
Richard Antonello, Chandan Singh, Shailee Jain +5
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is uncl…
CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents
Zhen Xiang, Aliyah R. Hsu, Austin V. Zane +6
Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes decisions are made. Clinical Decisi…
Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language Models
Aliyah R. Hsu, James Zhu, Zhichao Wang +11
LLMs have demonstrated impressive proficiency in generating coherent and high-quality text, making them valuable across a range of text-generation tasks. However, rigorous evaluati…
Adaptive Test-Time Intervention for Concept Bottleneck Models
Matthew Shen, Aliyah Hsu, Abhineet Agarwal +1
Concept bottleneck models (CBM) aim to improve model interpretability by predicting human level "concepts" in a bottleneck within a deep learning model architecture. However, how t…
Efficient Automated Circuit Discovery in Transformers using Contextual Decomposition
Aliyah R. Hsu, Georgia Zhou, Yeshwanth Cherapanamjeri +4
Automated mechanistic interpretation research has attracted great interest due to its potential to scale explanations of neural network internals to large models. Existing automate…