Search Results for author:
Found 14 papers, 9 papers with code
Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators.
How Susceptible are Large Language Models to Ideological Manipulation?
Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information.
Whose Emotions and Moral Sentiments Do Language Models Reflect?
We define the problem of affective alignment, which measures how LMs' emotional and moral tone represents those of different groups.
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities.
Characterizing Online Eating Disorder Communities with Large Language Models
We argue that social media platforms create a feedback loop that amplifies the growth of content and communities that promote eating disorders like anorexia and bulimia.
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Social media platforms are rife with politically charged discussions.
CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities
Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions.
ALCAP: Alignment-Augmented Music Captioner
Music captioning has gained significant attention in the wake of the rising prominence of streaming media platforms.
Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit
We collect discussions from a wide variety of topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc.
Infusing Knowledge from Wikipedia to Enhance Stance Detection
Stance detection infers a text author's attitude towards a target.
Speaker Turn Modeling for Dialogue Act Classification
Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue.
Ranked #2 on
Dialogue Act Classification
on Switchboard corpus
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings
We apply our method to a dataset of news articles about the COVID-19 pandemic.
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
Training Interpretable Convolutional Neural Networks towards Class-specific Filters
Convolutional neural networks (CNNs) have often been treated as “black-box” and successfully used in a range of tasks.
