8 citations · 14 across the 7 of their papers we have counts for
11 papers · 1 filter
WikiWhy: Answering and Explaining Cause-and-Effect Questions
Matthew Ho, Aditya Sharma, Justin Chang +4
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (…
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Sharon Levy, Emily Allaway, Melanie Subbiah +4
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. One such typ…
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation
Samhita Honnavalli, Aesha Parekh, Lily Ou +4
Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natur…
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
Kai Nakamura, Sharon Levy, Yi-Lin Tuan +2
A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multitu…
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
Alon Albalak, Sharon Levy, William Yang Wang
Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging do…
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
Sharon Levy, Kevin Mo, Wenhan Xiong +1
Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding t…