Search Results for author:
Found 16 papers, 8 papers with code
Foveate, Attribute, and Rationalize: Towards Safe and Trustworthy AI
Users' physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can lead to serious injury.
WikiWhy: Answering and Explaining Cause-and-Effect Questions
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging.
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe.
Mitigating Covertly Unsafe Text within Natural Language Systems
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences.
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation
Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains.
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities.
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains.
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
In this work, we present such a system for the emergent domain of COVID-19.
Investigating Memorization of Conspiracy Theories in Text Generation
In this work, we investigate the capability of language models to generate conspiracy theory text.
Modeling Disclosive Transparency in NLP Application Descriptions
Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable.
Investigating African-American Vernacular English in Transformer-Based Text Generation
The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts.
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment
The spread of COVID-19 has become a significant and troubling aspect of society in 2020.
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
Evaluating Transformer-Based Multilingual Text Classification
As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures.
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime.
