Natural Language Processing
Using Natural Language Processing to Measure and Improve Teaching Effectiveness: Evidence from Randomized Controlled Trials
Ailments of Alignment: Hurdles in Adapting Large Language Models to Human Demands
Hierarchical Approaches for Expanding NLP Language Coverage
Deconstructing Complex Events through Modeling Uncertainty, States, and Outcomes
Fighting the Global Social Media Infodemic: from Fake News to Harmful Content
Curriculum Learning: Scores, Plans, Dynamics, and NLP
Abstract: Curriculum learning is an effective and natural strategy in human learning. It plays an important role in challenging tasks such as language learning. However, current machine learning (ML) paradigms are mostly built upon repeatedly practicing the same training data/tasks with a random order, which is non-adaptive to the learning process. Moreover, they do not plan multiple learning stages in advance as humans.
Prof. Riedl will present virtually.
Zoom link: https://umd.zoom.us/j/98806584197?pwd=SXBWOHE1cU9adFFKUmN2UVlwUEJXdz09 (passcode if needed: clip)
The Quest for Automated Story Generation
Prof. Ettinger will present in person.
Zoom: https://umd.zoom.us/j/98806584197?pwd=SXBWOHE1cU9adFFKUmN2UVlwUEJXdz09 (passcode if needed: clip)
"Understanding" and prediction: Controlled examinations of meaning sensitivity in pre-trained models