42 citations · 249 across the 35 of their papers we have counts for
74 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 (…
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Wenda Xu, Yilin Tuan, Yujie Lu +3
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric? Existing learned metrics either perform unsatisfactorily or are restricted to t…
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Gyuwan Kim, Jinhyuk Lee, Barlas Oguz +4
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these proced…
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding
Josiah Ross, Luke Yoffe, Alon Albalak +1
Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency. This study explores the…
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…
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
An Yan, Jiacheng Li, Wanrong Zhu +3
Contrastive Language-Image Pretraining (CLIP) efficiently learns visual concepts by pre-training with natural language supervision. CLIP and its visual encoder have been explored o…