565 citations · 590 across the 18 of their papers we have counts for
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cs.CL2023
Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
Josh Magnus Ludan, Qing Lyu, Yue Yang +3
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability. To address this, we propose…
cs.CL2023
Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models
Liam Dugan, Anshul Wadhawan, Kyle Spence +3
Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however,…
cs.CL2023
Exploring the Curious Case of Code Prompts
Li Zhang, Liam Dugan, Hainiu Xu +1
Recent work has shown that prompting language models with code-like representations of natural language leads to performance improvements on structured reasoning tasks. However, su…