BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories

• URL: http://arxiv.org/abs/2604.17008v1
• Date: Sat, 18 Apr 2026 14:39:15 GMT
• Title: BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories
• Abstract summary: BiasedTales-ML is a large-scale parallel corpus of approximately 350,000 children's stories generated across eight typologically and culturally diverse languages.<n>Our analysis reveals substantial cross-lingual variability in narrative generation patterns, indicating that distributions observed in English do not always exhibit similar characteristics in other languages.<n>These findings highlight the limitations of English-centric evaluation for characterizing socially grounded narrative generation in multilingual settings.
• Score: 38.36264873832655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are increasingly used to generate narrative content, including children's stories, which play an important role in social and cultural learning. Despite growing interest in AI safety and alignment, most existing evaluations focus primarily on English, leaving the cross-lingual generalization of aligned behavior underexplored. In this work, we introduce BiasedTales-ML, a large-scale parallel corpus of approximately 350,000 children's stories generated across eight typologically and culturally diverse languages using a full-permutation prompting design. We propose a structured generator-extractor pipeline and a multi-dimensional distributional analysis framework to examine how narrative attributes vary across languages, models, and social conditions. Our analysis reveals substantial cross-lingual variability in narrative generation patterns, indicating that distributions observed in English do not always exhibit similar characteristics in other languages, particularly in lower-resource settings. At the narrative level, we identify recurring structural patterns involving character roles, settings, and thematic emphasis, which manifest differently across linguistic contexts. These findings highlight the limitations of English-centric evaluation for characterizing socially grounded narrative generation in multilingual settings. We release the dataset, code, and an interactive visualization tool to support future research on multilingual narrative analysis and evaluation.

Related papers

• On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation [79.18001266925245]
  LingT2I is a benchmark covering 10 widely used languages with 33K prompts.<n>We conduct a comprehensive cross-lingual analysis, uncovering linguistic inequality and language-dependent trade-offs.
  arXiv  Detail & Related papers  (2026-08-11T14:49:23Z)
• Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration [36.09273812207085]
  EspanStereo is a Spanish-language stereotype dataset spanning multiple Spanish-speaking countries across Europe and Latin America.<n>Our framework is adaptable to other languages and regions, offering a scalable path toward multilingual stereotype benchmarks.<n>This work broadens the scope of stereotype analysis in large language models and lays the groundwork for comprehensive cross-cultural bias evaluation.
  arXiv  Detail & Related papers  (2026-07-08T20:06:00Z)
• Multilinguality of Large Language Models From a Structural Perspective [55.097821802807566]
  Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data.<n>Our findings reveal that low-resource languages are structurally more different from English than high- and mid-resource languages.
  arXiv  Detail & Related papers  (2026-06-01T07:18:09Z)
• When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training [57.230355403478995]
  We investigate the development of language-agnostic concept spaces during pretraining of EuroLLM.<n>We find that shared concept spaces emerge early and continue to refine, but that alignment with them is language-dependent.<n>In contrast to prior work, our fine-grained manual analysis reveals that some apparent gains in translation quality reflect shifts in behavior.
  arXiv  Detail & Related papers  (2026-01-30T11:23:01Z)
• Feature-based analysis of oral narratives from Afrikaans and isiXhosa children [14.74555743937968]
  We analyse recorded stories from four- and five-year-old Afrikaans- and isiXhosa-speaking children.<n>We identify lexical diversity (unique words) and length-based features (mean utterance length) as indicators of typical development.<n>The use of specific verbs and auxiliaries associated with goal-directed storytelling is correlated with a reduced likelihood of requiring intervention.
  arXiv  Detail & Related papers  (2025-07-17T14:31:32Z)
• Decoding Memes: Benchmarking Narrative Role Classification across Multilingual and Multimodal Models [26.91963265869296]
  This work investigates the challenging task of identifying narrative roles in Internet memes.<n>It builds on an annotated dataset originally skewed toward the 'Other' class.<n> Comprehensive lexical and structural analyses highlight the nuanced, culture-specific, and context-rich language used in real memes.
  arXiv  Detail & Related papers  (2025-06-29T07:12:11Z)
• Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples [29.62231663945077]
  We introduce Cross-Lingual Semantic Discrimination (D), a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors.<n> CLSD measures an embedding model's ability to rank the true parallel sentence above semantically misleading but lexically similar alternatives.<n>Our experiments show that models fine-tuned for retrieval tasks benefit from pivoting through English, whereas bitext mining models perform best in direct cross-lingual settings.
  arXiv  Detail & Related papers  (2025-02-12T18:54:37Z)
• Exploring the Maze of Multilingual Modeling [2.0849578298972835]
  We present a comprehensive evaluation of three popular multilingual language models: mBERT, XLM-R, and GPT-3. Our findings reveal that while the amount of language-specific pretraining data plays a crucial role in model performance, we also identify other factors such as general resource availability, language family, and script type, as important features.
  arXiv  Detail & Related papers  (2023-10-09T04:48:14Z)
• Understanding Translationese in Cross-Lingual Summarization [106.69566000567598]
  Cross-lingual summarization (MS) aims at generating a concise summary in a different target language. To collect large-scale CLS data, existing datasets typically involve translation in their creation. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese.
  arXiv  Detail & Related papers  (2022-12-14T13:41:49Z)
• Cross-lingual Lifelong Learning [53.06904052325966]
  We present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm. We provide insights into what makes multilingual sequential learning particularly challenging. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata.
  arXiv  Detail & Related papers  (2022-05-23T09:25:43Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Multilingual AMR-to-Text Generation [22.842874899794996]
  We create multilingual AMR-to-text models that generate in twenty one different languages. For eighteen languages, based on automatic metrics, our multilingual models surpass baselines that generate into a single language. We analyse the ability of our multilingual models to accurately capture morphology and word order using human evaluation, and find that native speakers judge our generations to be fluent.
  arXiv  Detail & Related papers  (2020-11-10T22:47:14Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.