Search Results for author:
Found 16 papers, 6 papers with code
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines
Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e. g. inferring the writer’s intent), emotionally (e. g. feeling distrust), and behaviorally (e. g. sharing the news with their friends).
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.
Socratis: Are large multimodal models emotionally aware?
We further see that current captioning metrics based on large vision-language models also fail to correlate with human preferences.
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously.
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?
While a substantial body of prior work has explored adversarial example generation for natural language understanding tasks, these examples are often unrealistic and diverge from the real-world data distributions.
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
To help mitigate these issues, we create ToxiGen, a new large-scale and machine-generated dataset of 274k toxic and benign statements about 13 minority groups.
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof.
Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines
We propose Misinfo Reaction Frames (MRF), a pragmatic formalism for modeling how readers might react to a news headline.
GO FIGURE: A Meta Evaluation of Factuality in Summarization
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question.
Paragraph-level Commonsense Transformers with Recurrent Memory
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.
Detecting and Tracking Communal Bird Roosts in Weather Radar Data
The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years.
Social Bias Frames: Reasoning about Social and Power Implications of Language
We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others.
Discourse Understanding and Factual Consistency in Abstractive Summarization
We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary.
The Risk of Racial Bias in Hate Speech Detection
We investigate how annotators{'} insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations.
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
Early Fusion for Goal Directed Robotic Vision
Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline.
