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Evaluating Multimodal Narrative Understanding of Popular Hollywood Films — arXiv:2608.21430

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21430
License (as archived): CC0-1.0
Archived: 2026-10-18T07:38:41.557Z
SHA-256 of the archived text: 39950e6e9925c6756e244202da0174d211f06f471e6ab89f47d19488d61e8d31
Stable URL of this page: https://metraipolis.com/documento/dc_09ef9c2dcd97

How to cite

Evaluating Multimodal Narrative Understanding of Popular Hollywood Films — arXiv:2608.21430 — arXiv (en), https://arxiv.org/abs/2608.21430 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_09ef9c2dcd97 (Metraipolis archive dc_09ef9c2dcd97, sha256 39950e6e9925c6756e244202da0174d211f06f471e6ab89f47d19488d61e8d31)

BibTeX

@misc{dc09ef9c2dcd97,
  title = {Evaluating Multimodal Narrative Understanding of Popular Hollywood Films — arXiv:2608.21430},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21430},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_09ef9c2dcd97; sha256 39950e6e9925c6756e244202da0174d211f06f471e6ab89f47d19488d61e8d31},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21430. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multimodal language models increasingly show promise for enabling the large-scale computational analysis of film, opening up new avenues for learning about film history and the evolution of narrative techniques. But the creation of stable benchmarks built around Hollywood films is complicated by copyright protections. In this work, we address these concerns directly, by building a new collection of Hollywood films defined by two criteria: box office popularity (where we publish the first large-scale, open collection of weekly box office earnings reported by Variety magazine from 1922-1979); and likely public domain status (by researching copyright registrations and renewals in the US Catalog of Copyright Entries). We build a new multimodal MCQ benchmark on top of this collection that focuses on narrative elements that directly evaluate the abilities of models to inform meaningful research on film narrative; we find that many vision-language models struggle on this task (with many performing at near-chance levels of accuracy), while audio-visual models (including those that use audio in captioning scenes) reach a maximum accuracy of 61.1%, well below human-level performance.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-25 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2608.21430 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T07:37:52.641Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).