---
title: "BIASEDTALES-ML — paper by Ouyang et al. (2026)"
description: "Most studies of social bias in LLMs are English-only and rely on short-form probes. 29 references."
authors: ["Yuxuan Ouyang", "Yingfeng Luo", "JingBo Zhu", "Tong Xiao"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.862/"
doi: "10.18653/v1/2026.findings-acl.862"
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/ouyang-2026-biasedtales-ml-multilingual-dataset-analyzing/"
---
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# BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories

**Authors:** [Yuxuan Ouyang](https://md.research.elamur.ai/free/en/authors/yuxuan-ouyang/), [Yingfeng Luo](https://md.research.elamur.ai/free/en/authors/yingfeng-luo/), [JingBo Zhu](https://md.research.elamur.ai/free/en/authors/jingbo-zhu/) and [Tong Xiao](https://md.research.elamur.ai/free/en/authors/tong-xiao/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.862/) · [PDF](https://aclanthology.org/2026.findings-acl.862.pdf) · [DOI 10.18653/v1/2026.findings-acl.862](https://doi.org/10.18653/v1/2026.findings-acl.862) · [OpenAlex](https://openalex.org/W7166807542)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most studies of social bias in LLMs are English-only and rely on short-form probes. It is therefore unclear whether the attribute patterns seen in English long-form generation carry over to other languages. To test this, the authors built BIASEDTALES-ML, a parallel collection of about 350k children's stories. Three open-weight models wrote them natively in eight languages. The prompts were localized by native speakers rather than machine-translated. They also cover every combination of nationality, religion, social class, parent role and child gender.

A generator–extractor pipeline uses an LLM to pull out narrative features such as character traits, settings and themes. Distribution-based measures then compare groups, including Jensen–Shannon Divergence and log-odds lexical markers.

The main finding is that English attribute distributions often fail to hold in other languages, and the gap is largest for low-resource Swahili. In that setting, the smaller model writes fewer valid stories and shows stronger divergence. The authors argue this calls for multilingual, distributional evaluation of alignment.

## Key points
- The corpus holds 349,920 stories: 2,916 attribute configurations per language × 8 languages × 3 models × 5 samples. The configurations combine 27 nationalities, 6 religions, 2 classes, 3 parent roles and 3 child genders.
- The languages are English, Chinese, Japanese and Korean (no grammatical gender), Spanish, Russian and Arabic (grammatical gender), and Swahili (low-resource).
- The generators are Qwen-3-8B, Llama-3.1-8B and Llama-3.2-1B, run via vLLM at high temperature. Stories that fail automatic language identification are removed.
- Generation quality (Valid Story Rate) is plotted against bias strength (JSD), and the 1B model diverges most in low-resource settings.
- The dataset, code and the interactive Biased Tales Explorer are released on Hugging Face Spaces.

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

## Institutions

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## Cite

Cite as: Yuxuan Ouyang, Yingfeng Luo, JingBo Zhu and Tong Xiao. 2026. BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{ouyang2026biasedtales,
  title = {{BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories}},
  author = {Yuxuan Ouyang, Yingfeng Luo, JingBo Zhu and Tong Xiao},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.862},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.862}
}
```

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29. Category Values Nationality (N = 27) Americas: American, Mexican, Brazilian, Argentine Europe: British, French, German, Spanish, Russian, Ukrainian Asia: Chinese, Japanese, Korean, Indian, Filipino, Indonesian, Thai, Vietnamese, Sri Lankan Middle East: Iranian, Egyptian, Saudi Africa: Nigerian, Ethi… [find](https://scholar.google.com/scholar?q=Category+Values+Nationality+%28N+%3D+27%29+Americas%3A+American%2C+Mexican%2C+Brazilian%2C+Argentine+Europe%3A+British%2C+French%2C+German%2C+Spanish%2C+Russian%2C+Ukrainian+Asia%3A+Chinese%2C+Japanese%2C+Korean%2C+Indian%2C+Filipino%2C+I)

*Updated 1 November 2026*