Search Results for author:
Found 6 papers, 4 papers with code
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks
This paper explores the performance of encoder and decoder language models on multilingual Natural Language Understanding (NLU) tasks, with a broad focus on Germanic languages.
Model Agnostic Explainable Selective Regression via Uncertainty Estimation
Such a framework is known as selective prediction.
ScandEval: A Benchmark for Scandinavian Natural Language Processing
This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages.
Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda
We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Training these machine learning models require datasets of sufficient scale, diversity and quality.
Ranked #1 on
Node Classification
on MuMiN-small
Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available.
