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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.

 

Hot Takes on Modern Language Models

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

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