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SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models

Paper · 2026 · Marco Valentino, Leonardo Ranaldi, Giulia Pucci et al.

Summary

This shared task asks whether large language models can judge if an argument is logically valid without being swayed by whether its content sounds true. This bias is called the content effect. The organisers built a new multilingual dataset of syllogisms that crosses validity with plausibility. It covers 24 syllogistic schemes in ten languages. Four subtasks were defined: English and multilingual validity classification, each in a clean version and a version that mixes in irrelevant premises. In the noisy versions, systems must also pick out which premises actually matter. Ranking used a combined score that rewards accuracy and penalises content bias.

Zero-shot tests showed that older open models carry a strong content effect. Newer generations largely overcome it. The best participant systems handed inference to symbolic solvers and scored perfectly in English. Distracting premises and non-English inputs still raised bias noticeably. The task therefore supports neuro-symbolic pipelines and points to multilingual, longer-context reasoning as open problems.

Key points

  • The competition ran on Codabench. It drew about 250 participants, more than 3000 submissions and 35 system description papers.
  • Metrics included accuracy, Total Content Effect (TCE, the mean of intra- and cross-plausibility effects) and premise-selection F1 for the noisy subtasks. The ranking metric was Combined Score: CS = ACC / (1 + ln(1 + TCE)). In the noisy subtasks, the numerator is the average of ACC and F1.
  • Baselines differed by model generation:
    • Llama 3 and Qwen 2.5 showed pervasive content bias, and larger size did not reliably help.
    • Qwen 3 and Phi-4 exceeded 95 accuracy with lower bias.
  • In Subtask 1, several systems reached 100% accuracy with zero TCE (CS = 100). In Subtask 2, two systems scored perfectly. These top systems mostly relied on pseudo-formal decomposition, often combined with architectural changes.
  • Multilingual settings were harder:
    • In Subtask 3, systems above 90% accuracy still showed TCE of roughly 5–11.
    • In Subtask 4, accuracy and F1 were often 85–91%, with TCE around 3–5.

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

🎓University of EdinburghEdinburgh · United Kingdom359 works · 77,352 citations🎓University of SheffieldSheffield · United Kingdom113 works · 21,542 citations🎓University of ManchesterManchester · United Kingdom87 works · 44,938 citations🔬Idiap Research InstituteMartigny-Combe · Switzerland32 works · 1,954 citations🎓University of AberdeenAberdeen · United Kingdom20 works · 1,385 citations🎓University of Rome Tor VergataRome · Italy15 works · 1,321 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 3Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Marco Valentino, Leonardo Ranaldi, Giulia Pucci, Federico Ranaldi and André Freitas. 2026. SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.semeval-1.450

@inproceedings{valentino2026semeval,
  title = {{SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models}},
  author = {Marco Valentino, Leonardo Ranaldi, Giulia Pucci, Federico Ranaldi and André Freitas},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.450},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.450}
}

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