8 citations · 8 across the 5 of their papers we have counts for
5 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…
End-to-End Spoken Language Understanding for Generalized Voice Assistants
Michael Saxon, Samridhi Choudhary, Joseph P. McKenna +1
End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model. Previous work in this area has focused on target…
Investigating Memorization of Conspiracy Theories in Text Generation
Sharon Levy, Michael Saxon, William Yang Wang
The adoption of natural language generation (NLG) models can leave individuals vulnerable to the generation of harmful information memorized by the models, such as conspiracy theor…
Semantic Complexity in End-to-End Spoken Language Understanding
Joseph P. McKenna, Samridhi Choudhary, Michael Saxon +2
End-to-end spoken language understanding (SLU) models are a class of model architectures that predict semantics directly from speech. Because of their input and output types, we re…