37 citations · 78 across the 38 of their papers we have counts for
7 papers · 1 filter
Leveraging semantic similarity for experimentation with AI-generated treatments
Lei Shi, David Arbour, Raghavendra Addanki +2
Large Language Models (LLMs) enable a new form of digital experimentation where treatments combine human and model-generated content in increasingly sophisticated ways. The main me…
Handling Missing Responses under Cluster Dependence with Applications to Language Model Evaluation
Zhenghao Zeng, David Arbour, Avi Feller +3
Human annotations play a crucial role in evaluating the performance of GenAI models. Two common challenges in practice, however, are missing annotations (the response variable of i…
Regularizing Extrapolation in Causal Inference
David Arbour, Harsh Parikh, Bijan Niknam +3
Many common estimators in machine learning and causal inference are linear smoothers, where the prediction is a weighted average of the training outcomes. Some estimators, such as…
Relational Causal Discovery with Latent Confounders
Matteo Negro, Andrea Piras, Ragib Ahsan +2
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery…
Sequential Causal Discovery with Noisy Language Model Priors
Prakhar Verma, David Arbour, Sunav Choudhary +3
Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data often arrive in batches, are subjec…
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries
Vishakh Padmakumar, Zichao Wang, David Arbour +1
While large language models (LLMs) are increasingly capable of handling longer contexts, recent work has demonstrated that they exhibit the "lost in the middle" phenomenon (Liu et…
Evaluation and Incident Prevention in an Enterprise AI Assistant
Akash V. Maharaj, David Arbour, Daniel Lee +6
Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a co…