4 citations · 6 across the 4 of their papers we have counts for
5 papers
Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models
Chantal Shaib, Vinith M. Suriyakumar, Levent Sagun +2
For an LLM to correctly respond to an instruction it must understand both the semantics and the domain (i.e., subject area) of a given task-instruction pair. However, syntax can al…
Measuring AI "Slop" in Text
Chantal Shaib, Tuhin Chakrabarty, Diego Garcia-Olano +1
AI "slop" is an increasingly popular term used to describe low-quality AI-generated text, but there is currently no agreed upon definition of this term nor a means to measure its o…
Detection and Measurement of Syntactic Templates in Generated Text
Chantal Shaib, Yanai Elazar, Junyi Jessy Li +1
Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repeti…
Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores
Chantal Shaib, Venkata S. Govindarajan, Joe Barrow +4
The diversity across outputs generated by LLMs shapes perception of their quality and utility. High lexical diversity is often desirable, but there is no standard method to measure…
How Much Annotation is Needed to Compare Summarization Models?
Chantal Shaib, Joe Barrow, Alexa F. Siu +2
Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace. In practice one may now…