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Nanyun Peng

6 papers hereh-index 5159 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author3

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.HC1
  • cs.LG1
same name
  • Nanyun Peng — 114 papers, h 50
  • Nanyun Peng — 56 papers, h 20
  • Nanyun Peng — 17 papers, h 3
  • Nanyun Peng — 12 papers, h 8
  • Nanyun Peng — 8 papers, h 6
  • Nanyun Peng — 8 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedAre Large Language Models Capable of Generating Human-Level Narratives?

5 citations · 6 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024

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.…

cs.CL2024★ 5 cited

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…

cs.CL2024★ 1 cited

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…

cs.CL2023

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.