5 citations · 6 across the 5 of their papers we have counts for
6 papers
Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging
Ruichen Zheng, Yihe Wang, Fabrice Y Harel-Canada +4
LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may not guarantee valid measurem…
CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support
Masahiro Yoshida, Bingxuan Li, Songyan Zhao +4
We propose CoLyricist, an AI-assisted lyric writing tool designed to support the typical workflows of experienced lyricists and enhance their creative efficiency. While lyricists h…
Explaining Mixtures of Sources in News Articles
Alexander Spangher, James Youn, Matt DeButts +3
Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing.…
Are Large Language Models Capable of Generating Human-Level Narratives?
Yufei Tian, Tenghao Huang, Miri Liu +5
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narr…
Measuring Psychological Depth in Language Models
Fabrice Harel-Canada, Hanyu Zhou, Sreya Muppalla +4
Evaluations of creative stories generated by large language models (LLMs) often focus on objective properties of the text, such as its style, coherence, and diversity. While these…
Tracking the Newsworthiness of Public Documents
Alexander Spangher, Emilio Ferrara, Ben Welsh +3
Journalists must find stories in huge amounts of textual data (e.g. leaks, bills, press releases) as part of their jobs: determining when and why text becomes news can help us unde…