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Patrik Haslum

Patrik Haslum has 2 papers in our library, in 2026, most of them on Text generation, Language models & pretraining; often with Hanna Suominen, Richard Susilo.

2 papers in our library · 2026

OpenAlex · h-index 27 · 136 works · 2,828 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #11645

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Australian National UniversityCanberra · Australia2001–2026 · 76 paperscurrent🔬Data61Sydney · Australia2007–2021 · 43 papers🎓Linköping UniversityLinköping · Sweden2000–2008 · 9 papers🏛️Commonwealth Scientific and Industrial Research OrganisationCanberra · Australia2017–2021 · 7 papers🎓University of BresciaBrescia · Italy2006–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Text generation · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Text-to-Text Automatic Story Generation: A Survey (2026 · 1 citation)
    This survey maps recent work on generating stories from text input with little or no human effort. The task still struggles with long-range coherence, consistent characters, varied storylines and plot control. The authors ran a systematic search covering January 2021 to April 2025. They queried the ACL Anthology by…
  2. Contextual Diversity Measure (CDM) for Controllable Story Generation in Large Language Models (2026)
    The paper targets a gap in controllable text generation: there is no metric for how varied a set of generated scenarios is when they all have to follow the same abstract meaning.

 Co-authors here

2 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Updated

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