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Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation

Paper · 2026 · Qianli Wang, Van Bach Nguyen, Yihong Liu et al.

Summary

Counterfactual explanations are small edits to an input that flip a classifier's prediction. LLMs produce them well in English, but their quality in other languages had not been studied. To examine this, the authors evaluate three open instruction-tuned LLMs on XNLI and SIB200, two multilingual classification datasets, covering Arabic, English, German, Hindi, Spanish and Swahili (six languages in total). The model being explained is a fine-tuned multilingual BERT.

They compare two approaches. The first writes counterfactuals directly in the target language. The second creates English counterfactuals and has the same LLM translate them. Both use an existing one-shot Chain-of-Thought method with English prompts. Translated counterfactuals flip labels more often but require far larger edits, and they still lag behind the English originals. Direct generation works best for high-resource European languages.

The study also compares edit patterns across languages, catalogues recurring errors, and tests counterfactuals as training data. The work shows how far LLM-based explanations transfer beyond English and where they break down.

Key points

  • Setup: Qwen2.5-7B, Gemma3-27B and Llama3.3-70B were evaluated with label flip rate (LFR), perplexity and similarity based on multilingual sentence embeddings. Code and results are at github.com/qiaw99/multicfe.
  • Translation-based counterfactuals beat direct ones on LFR. However, translation quality does not strongly predict validity, so machine translation is not presented as the ideal route.
  • Edits in English, German and Spanish counterfactuals target very similar words, suggesting shared perturbation strategies across languages.
  • Four error types recur across languages: copy-paste, negation, inconsistency and language confusion. Copy-paste is the most frequent.
  • In counterfactual data augmentation (CDA), multilingual CDA outperforms cross-lingual CDA, most clearly for lower-resource languages. Noisy counterfactuals limit consistent gains in accuracy and robustness.

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

🎓Ludwig-Maximilians-Universität MünchenMunich · Germany107 works · 56,478 citations🔬German Research Centre for Artificial IntelligenceKaiserslautern · Germany77 works · 29,479 citations🎓Munich Center for Machine LearningMunich · Germany57 works · 1,094 citations🎓Technische Universität BerlinBerlin · Germany22 works · 957 citations🎓Philipps University of MarburgMarburg · Germany8 works · 226 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 828 works

Cite

Cite as Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller and Vera Schmitt. 2026. Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation. Proceedings of the 64th Annual Meeting 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. DOI 10.18653/v1/2026.acl-long.12

@inproceedings{wang2026parallel,
  title = {{Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation}},
  author = {Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller and Vera Schmitt},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.12},
  url = {https://doi.org/10.18653/v1/2026.acl-long.12}
}

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