1 citations · 2 across the 4 of their papers we have counts for
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RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
Liam Dugan, Alyssa Hwang, Filip Trhlik +5
Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more). However, very few of these detectors are evaluated on shar…
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,…
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…
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
Daphne Ippolito, Liam Dugan, Emily Reif +3
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural…