4 papers
Who's Asking? Evaluating LLM Robustness to Inquiry Personas in Factual Question Answering
Nil-Jana Akpinar, Chia-Jung Lee, Vanessa Murdock +1
Large Language Models (LLMs) should answer factual questions truthfully, grounded in objective knowledge, regardless of user context such as self-disclosed personal information, or…
A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation
Riccardo Fogliato, Pratik Patil, Mathew Monfort +1
Model performance evaluation is a critical and expensive task in machine learning and computer vision. Without clear guidelines, practitioners often estimate model accuracy using a…
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
Ira Globus-Harris, Declan Harrison, Michael Kearns +2
Crowdsourced machine learning on competition platforms such as Kaggle is a popular and often effective method for generating accurate models. Typically, teams vie for the most accu…
On the Effect of Image Resolution on Semantic Segmentation
Ritambhara Singh, Abhishek Jain, Pietro Perona +2
High-resolution semantic segmentation requires substantial computational resources. Traditional approaches in the field typically downscale the input images before processing and t…