Evidence map›Paper›PMID 40875804›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Echoes in AI: Quantifying lack of plot diversity in LLM outputs.

Weijia Xu, Nebojsa Jojic, Sudha Rao, Chris Brockett, Bill Dolan

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Weijia XuMicrosoft Research, Redmond, WA 98052.ORCID 0000-0002-5732-8926
Nebojsa JojicMicrosoft Research, Redmond, WA 98052.ORCID 0009-0009-7115-2267
Sudha RaoMicrosoft Research, Redmond, WA 98052.
Chris BrockettMicrosoft Research, Redmond, WA 98052.
Bill DolanMicrosoft Research, Redmond, WA 98052.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With rapid advances in large language models (LLMs), there has been an increasing application of LLMs in creative content ideation and generation. A critical question emerges: can current LLMs provide ideas that are diverse enough to truly bolster collective creativity? We examine two state-of-the-art LLMs, GPT-4 and LLaMA-3, on story generation and discover that LLM-generated stories often consist of plot elements that are echoed across a number of generations. To quantify this phenomenon, we introduce the

Indexed as

Artificial IntelligenceCreativityLanguageHumansAIcreativitydeep learningLLMtext generation

Identifiers

PMID40875804
PMCPMC12415252

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.