119 citations · 160 across the 21 of their papers we have counts for
14 papers · 1 filter
RTTC: Reward-Guided Collaborative Test-Time Compute
J. Pablo Muñoz, Jinjie Yuan
Test-Time Compute (TTC) has emerged as a powerful paradigm for enhancing the performance of Large Language Models (LLMs) at inference, leveraging strategies such as Test-Time Train…
ModelCitizens: Representing Community Voices in Online Safety
Ashima Suvarna, Christina Chance, Karolina Naranjo +4
Automatic toxic language detection is critical for creating safe, inclusive online spaces. However, it is a highly subjective task, with perceptions of toxic language shaped by com…
AI Debate Aids Assessment of Controversial Claims
Salman Rahman, Sheriff Issaka, Ashima Suvarna +11
As AI grows more powerful, it will increasingly shape how we understand the world. But with this influence comes the risk of amplifying misinformation and deepening social divides-…
Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English
Runtao Zhou, Guangya Wan, Saadia Gabriel +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, leading to their widespread deployment. However, recent studies have highlighted concerni…
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
Jaeyoung Lee, Ximing Lu, Jack Hessel +5
Given the growing influx of misinformation across news and social media, there is a critical need for systems that can provide effective real-time verification of news claims. Larg…
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?
Saadia Gabriel, Hamid Palangi, Yejin Choi
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 t…
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi +3
Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on…
Can Machines Learn Morality? The Delphi Experiment
Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula +12
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof. Yet, teaching morality to machines is a formidable…
Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines
Saadia Gabriel, Skyler Hallinan, Maarten Sap +4
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 behaviora…
GO FIGURE: A Meta Evaluation of Factuality in Summarization
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha +2
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This majo…
Paragraph-level Commonsense Transformers with Recurrent Memory
Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz +3
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text. A recent model, COMET, can generate such implicit common…
Social Bias Frames: Reasoning about Social and Power Implications of Language
Maarten Sap, Saadia Gabriel, Lianhui Qin +3
Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the c…
Discourse Understanding and Factual Consistency in Abstractive Summarization
Saadia Gabriel, Antoine Bosselut, Jeff Da +5
We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. Our work addresses current…
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
Aida Amini, Saadia Gabriel, Peter Lin +3
We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challe…