3 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation
Zini Yang, Emily Wenger, Richard So
When a large language model (LLM) writes Harry Potter fanfiction, it reliably produces fundamental elements of the Hogwarts universe, such as recognizable places and characters. Hu…
Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling
Peiqi Sui, Yutong Zhu, Tianyi Cheng +4
LLMs have so far failed both to generate consistently compelling stories and to recognize this failure--on the leading creative-writing benchmark (EQ-Bench), LLM judges rank zero-s…
Critical Confabulation: Can LLMs Hallucinate for Social Good?
Peiqi Sui, Eamon Duede, Hoyt Long +1
LLMs hallucinate, yet some confabulations can have social affordances if carefully bounded. We propose critical confabulation (inspired by critical fabulation from literary and soc…
Generative AI & Fictionality: How Novels Power Large Language Models
Edwin Roland, Richard Jean So
Generative models, like the one in ChatGPT, are powered by their training data. The models are simply next-word predictors, based on patterns learned from vast amounts of pre-exist…
KRISTEVA: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning
Peiqi Sui, Juan Diego Rodriguez, Philippe Laban +5
Each year, tens of millions of essays are written and graded in college-level English courses. Students are asked to analyze literary and cultural texts through a process known as…
Confabulation: The Surprising Value of Large Language Model Hallucinations
Peiqi Sui, Eamon Duede, Sophie Wu +1
This paper presents a systematic defense of large language model (LLM) hallucinations or 'confabulations' as a potential resource instead of a categorically negative pitfall. The s…