12 citations · 24 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting
Gauri Kambhatla, Chantal Shaib, Venkata Govindarajan
Fine-grained personas have recently been used for generating 'diverse' synthetic data for pre-training and supervised fine-tuning of Large Language Models (LLMs). In this work, we…
Who Taught You That? Tracing Teachers in Model Distillation
Somin Wadhwa, Chantal Shaib, Silvio Amir +1
Model distillation -- using outputs from a large teacher model to teach a small student model -- is a practical means of creating efficient models for a particular task. We ask: Ca…