---
title: "LongVideoBench — paper by Wu et al. (2024)"
description: "Each question first describes a referring query that points to one or more moments of the video (the… 39 references, cited by 98 papers here."
authors: ["Haoning Wu", "Dongxu Li", "Bei Chen", "Junnan Li"]
year: 2024
venue: "arXiv:2407.15754"
source: "https://arxiv.org/abs/2407.15754"
doi: "10.48550/arxiv.2407.15754"
arxiv: "2407.15754"
licence: "CC BY-NC-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2024-longvideobench-benchmark-long-context-interleaved/"
---
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# LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding

**Authors:** [Haoning Wu](https://md.research.elamur.ai/free/en/authors/haoning-wu/), [Dongxu Li](https://md.research.elamur.ai/free/en/authors/dongxu-li/), [Bei Chen](https://md.research.elamur.ai/free/en/authors/bei-chen/) and [Junnan Li](https://md.research.elamur.ai/free/en/authors/junnan-li/) · **Year:** 2024 · **Venue:** [arXiv:2407.15754](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ([NeurIPS 2024](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2024/)) · **Licence:** [CC BY-NC-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2407.15754) · [PDF](https://arxiv.org/pdf/2407.15754) · [DOI 10.48550/arxiv.2407.15754](https://doi.org/10.48550/arxiv.2407.15754) · [arXiv 2407.15754](https://arxiv.org/abs/2407.15754) · [OpenAlex](https://openalex.org/W4415798209)

**Citations:** 68 citations (OpenAlex, 2026-10-14) · [98](https://md.research.elamur.ai/free/en/papers/wu-2024-longvideobench-benchmark-long-context-interleaved/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Benchmarks for long-context models are mostly text-only. Existing video QA sets also suffer from single-frame bias: models score about the same whether they see a few frames or many. LongVideoBench addresses this with a multiple-choice benchmark of web-collected, subtitled videos lasting up to an hour, in which visual frames and subtitles are interleaved. Its core task, *referring reasoning*, works in two steps. Each question first describes a referring query that points to one or more moments of the video (the referred context), and then asks about details there. Answering requires the model to locate those moments in a long input and then reason over them. Questions come at two levels: perception of a single moment, and relations across several moments. Proprietary models such as GPT-4o, Gemini-1.5-Pro and GPT-4-Turbo find the benchmark hard, and open-source models trail them further. Accuracy rises only for models that can actually use more frames, which makes the benchmark a useful yardstick for future long-context LMMs.

## Key points
- **Scale and comparison:** 3,763 videos and 6,678 human-annotated multiple-choice questions. The test answers are hidden to limit contamination.
- **Video coverage:** themes include movies, news, life and knowledge. Videos fall into four duration bins: 8–15 s, 15–60 s, 3–10 min and 15–60 min. The average length is 473 s, longer than EgoSchema or MovieChat-1K.
- **Question categories:** 17 fine-grained categories split into two levels:
  - Perception (L1): 3,204 questions.
  - Relation (L2): 3,474 questions.
  - L2 is harder overall. Sequence of Scenes (SSS), where distractors are reorderings of the true scene order, is the weakest category for every model.
- **Open-source models:** PLLaVA-34B ranks highest. Larger PLLaVA variants (13B, 34B) gain 5.9% and 14.3% over a smaller counterpart trained on the same data, indicating that scaling the language model helps.
- **Effect of moment position:** accuracy falls when the referred moment sits near the start or middle of the video, and the drop grows with video length. This mirrors needle-in-a-haystack findings for text.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Haoning Wu, Dongxu Li, Bei Chen and Junnan Li. 2024. LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding. arXiv:2407.15754.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wu2024longvideobench,
  title = {{LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding}},
  author = {Haoning Wu, Dongxu Li, Bei Chen and Junnan Li},
  year = {2024},
  doi = {10.48550/arxiv.2407.15754},
  eprint = {2407.15754},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.15754}
}
```

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*Updated 2 November 2026*