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

5 citations · 7 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

NewsEdits 2.0: Learning the Intentions Behind Updating News

Alexander Spangher, Kung-Hsiang Huang, Hyundong Cho +1

As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts. In this work, we hypothesize that linguistic featur…

cs.CL2024★ 2 cited

PatentEdits: Framing Patent Novelty as Textual Entailment

Ryan Lee, Alexander Spangher, Xuezhe Ma

A patent must be deemed novel and non-obvious in order to be granted by the US Patent Office (USPTO). If it is not, a US patent examiner will cite the prior work, or prior art, tha…

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

NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews

Alexander Spangher, Michael Lu, Sriya Jeslyn Kalyan +3

Large Language Models (LLMs) have demonstrated impressive capabilities in generating coherent text but often struggle with grounding language and strategic dialogue. To address thi…

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…