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Found 4 papers, 2 papers with code
Eliciting Bias in Question Answering Models through Ambiguity
We develop two sets of questions for closed and open domain questions respectively, which use ambiguous questions to probe QA models for bias.
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention.
Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI
Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cram\'er-Rao bound.
Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain Question Answering
For humans and computers, the first step in answering an open-domain question is retrieving a set of relevant documents from a large corpus.
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