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Found 6 papers, 1 papers with code
BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?
These insights highlight the potential benefits of RLHF fine-tuning for language models within limited data, enhancing their ability to maintain narrative focus and coherence while adhering better to initial instructions in storytelling tasks.
Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News
In this paper, we explore the perceived perception of cyclists within news headlines.
A Marker-based Neural Network System for Extracting Social Determinants of Health
The major finding of this study is that the multi-stage pipeline effectively extracts SDoH information from clinical notes.
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models
Our findings emphasize the biases in chemical NER models, urging practitioners to account for these biases in downstream applications.
UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks
The act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those that experience it.
Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street
Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop.
