From the 1 of 5 linked papers with an AI index.
5 papers
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Albert Gong, Kyuseong Choi, Abhineet Agarwal +5
The paper presents ORCA-bench, a benchmark that evaluates large language model agents on on-call root cause analysis tasks using real telemetry data from a live microservice system…
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
Abhineet Agarwal, Fange Xiao, Rebecca Barter +3
As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on th…
ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs
Landon Butler, Abhineet Agarwal, Justin Singh Kang +3
Large Language Models (LLMs) have achieved remarkable performance by capturing complex interactions between input features. To identify these interactions, most existing approaches…
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
Abhineet Agarwal, Ana M. Kenney, Yan Shuo Tan +2
Random forests (RFs) are among the most popular supervised learning algorithms due to their nonlinear flexibility and ease-of-use. However, as black box models, they can only be in…
SPEX: Scaling Feature Interaction Explanations for LLMs
Justin Singh Kang, Landon Butler, Abhineet Agarwal +4
Large language models (LLMs) have revolutionized machine learning due to their ability to capture complex interactions between input features. Popular post-hoc explanation methods…