---
title: "CAIRE: Cultural Attribution of Images with Retrieval (2026)"
description: "The authors present CAIRE, an evaluation metric for \"visual cultural attribution.\" Given an image and a… 57 references, cited by 1 paper here."
authors: ["Arnav Yayavaram", "Siddharth Yayavaram", "Simran Khanuja", "Michael Saxon", "Graham Neubig"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.389/"
doi: "10.18653/v1/2026.eacl-long.389"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yayavaram-2026-caire-cultural-attribution-images-retrieval/"
---
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# CAIRE: Cultural Attribution of Images with Retrieval

**Authors:** Arnav Yayavaram, Siddharth Yayavaram, [Simran Khanuja](https://md.research.elamur.ai/free/en/authors/simran-khanuja/), [Michael Saxon](https://md.research.elamur.ai/free/en/authors/michael-saxon/) and [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.389/) · [PDF](https://aclanthology.org/2026.eacl-long.389.pdf) · [DOI 10.18653/v1/2026.eacl-long.389](https://doi.org/10.18653/v1/2026.eacl-long.389) · [OpenAlex](https://openalex.org/W7140139846)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/yayavaram-2026-caire-cultural-attribution-images-retrieval/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Text-to-image systems often depict cultures unevenly, favouring Western imagery or producing stereotyped and inaccurate pictures. Progress on this has been slowed by the lack of reliable ways to measure it. The authors present CAIRE, an evaluation metric for "visual cultural attribution." Given an image and a list of free-text culture labels chosen by the user (for example regions, ethnic groups or musical traditions), it rates how relevant the image is to each label on a 1–5 scale. CAIRE works in two stages:

- **Linking:** it connects what appears in the image to entities in the multilingual BabelNet knowledge base through embedding retrieval.
- **Scoring:** a vision-language model reads the image together with Wikipedia text about the linked entities and assigns a graded score for each label.

To evaluate the metric, the authors built a dataset of rare but culturally meaningful items, where CAIRE beats all baselines by 22 F1 points. They also built two human-rated sets of culturally universal concepts, one generated and one natural. The metric matters because it avoids fixed country-based definitions of culture and works on both natural and generated images.

## Key points
- **Pipeline components:** BabelNet (about 6M entities) serves as the knowledge base and mSigLIP as the image-text encoder. Image and text embeddings are stored in separate FAISS indices, and retrieval returns the top K=20 entities. The final entity is chosen by matching the image against each candidate's lemma embedding.
- **Scoring step:** Qwen-VL 2.5-Instruct produces the scores using a rubric. Its decoding is restricted to the tokens 1–5, and the final value is derived from the model's probabilities over those scores.
- **Benchmark results:** On the rare culturally salient items dataset, labelled with GPT-4o and checked against Wikipedia, CAIRE improves on baselines by 22 F1 points.
- **Agreement with humans:** Pearson correlation with human 5-point Likert ratings is 0.56 on T2I-generated images and 0.66 on naturally occurring images.
- **Batch-level use and code:** Applied across batches of T2I outputs, CAIRE shows systematically higher scores for Western countries such as the US, UK, Australia and Germany. The code is available at github.com/siddharthyayavaram/CAIRE.

*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: Arnav Yayavaram, Siddharth Yayavaram, Simran Khanuja, Michael Saxon and Graham Neubig. 2026. CAIRE: Cultural Attribution of Images with Retrieval. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{yayavaram2026caire,
  title = {{CAIRE: Cultural Attribution of Images with Retrieval}},
  author = {Arnav Yayavaram, Siddharth Yayavaram, Simran Khanuja, Michael Saxon and Graham Neubig},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.389},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.389}
}
```

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*Updated 31 October 2026*