267 citations · 297 across the 14 of their papers we have counts for
15 papers
Evaluating Multimodal Narrative Understanding of Popular Hollywood Films
David Bamman, Kent K. Chang, Allison Cooper +7
Multimodal language models increasingly show promise for enabling the large-scale computational analysis of film, opening up new avenues for learning about film history and the evo…
Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes
Li Lucy, Camilla Griffiths, Sarah Levine +3
Conventional bag-of-words approaches for topic modeling, like latent Dirichlet allocation (LDA), struggle with literary text. Literature challenges lexical methods because narrativ…
Multimodal Conversation Structure Understanding
Kent K. Chang, Mackenzie Hanh Cramer, Anna Ho +3
While multimodal large language models (LLMs) excel at dialogue, whether they can adequately parse the structure of conversation -- conversational roles and threading -- remains un…
Subversive Characters and Stereotyping Readers: Characterizing Queer Relationalities with Dialogue-Based Relation Extraction
Kent K. Chang, Anna Ho, David Bamman
Television is often seen as a site for subcultural identification and subversive fantasy, including in queer cultures. How might we measure subversion, or the degree to which the d…
On Classification with Large Language Models in Cultural Analytics
David Bamman, Kent K. Chang, Li Lucy +1
In this work, we survey the way in which classification is used as a sensemaking practice in cultural analytics, and assess where large language models can fit into this landscape.…
AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters
Li Lucy, Suchin Gururangan, Luca Soldaini +4
Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation. However, decisions around what data is retained or r…