23 citations · 81 across the 18 of their papers we have counts for
20 papers
Looking for the Inner Music: Probing LLMs' Understanding of Literary Style
Rebecca M. M. Hicke, David Mimno
Recent work has demonstrated that language models can be trained to identify the author of much shorter literary passages than has been thought feasible for traditional stylometry.…
Stronger Random Baselines for In-Context Learning
Gregory Yauney, David Mimno
Evaluating the in-context learning classification performance of language models poses challenges due to small dataset sizes, extensive prompt-selection using the validation set, a…
[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs
Rebecca M. M. Hicke, David Mimno
Coreference annotation and resolution is a vital component of computational literary studies. However, it has previously been difficult to build high quality systems for fiction. C…
The Afterlives of Shakespeare and Company in Online Social Readership
Maria Antoniak, David Mimno, Rosamond Thalken +3
The growth of social reading platforms such as Goodreads and LibraryThing enables us to analyze reading activity at very large scale and in remarkable detail. But twenty-first cent…
Data Similarity is Not Enough to Explain Language Model Performance
Gregory Yauney, Emily Reif, David Mimno
Large language models achieve high performance on many but not all downstream tasks. The interaction between pretraining data and task data is commonly assumed to determine this va…
T5 meets Tybalt: Author Attribution in Early Modern English Drama Using Large Language Models
Rebecca M. M. Hicke, David Mimno
Large language models have shown breakthrough potential in many NLP domains. Here we consider their use for stylometry, specifically authorship identification in Early Modern Engli…