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Evaluating Language Model Pluralism through In-the-wild Crowd Discussions

Paper · 2026 · Gagan Mundada, Rohan Surana, Nandhini Swaminathan et al.

Summary

LLMs tuned with preference alignment often lean toward particular stances on subjective questions. Yet pluralism has mostly been tested with multiple-choice or rating formats, and the few open-ended benchmarks depend on costly crowd annotation for every new domain. The authors propose PluralEval, which builds reference sets automatically from real online discussions. Each comment is split into atomic opinion units. Paraphrased units are then clustered into distinct viewpoints, and a model's free-form answer is scored by how many of these viewpoints it covers. This approximates an unweighted Overton window. To apply the method, they release WildScope, a Reddit corpus spanning moral, economic, and political questions. Across six commercial models, they find a lopsided form of sycophancy. When a user states a belief, models mostly drop opposing views rather than simply amplifying the user's side. Stated bias also hurts the models' ability to judge which opinions are popular. The framework offers annotation-free and scalable pluralism auditing, though its findings are tied to the English-speaking Reddit population.

Key points

  • WildScope contains 1,212 threads from 2019 Reddit archives, collected via Pushshift, with 8–70 comments each. The sources are r/AmItheAsshole, r/AskEconomics, and r/PoliticalDiscussion. It is released under CC BY-NC 4.0 at github.com/GaganVM/ACL26-PluralEval.
  • Six models were evaluated: GPT-4.1, GPT-4.1-mini, Gemini 2.5 Flash Lite, Gemini 2.0 Flash, Claude Haiku 3.0, and Claude Haiku 3.5.
  • In human evaluation, annotators preferred PluralEval's clustering 65.5% of the time.
  • Opinion-popularity ranking on AITA gets worse when the prompt favors an unpopular view.
    • Spearman ρ falls from 0.447 to 0.310 for Gemini 2.5 Flash Lite and from 0.414 to 0.293 for Haiku 3.0.
    • Haiku 3.5 is the most robust, dropping only from 0.452 to 0.438.
  • In pairwise "which gets more upvotes" tests, models tend to side with the opinion the prompt favors rather than the one that is actually more popular.

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

🔬Allen Institute for Artificial IntelligenceSeattle · United States275 works · 97,634 citations🎓University of California San DiegoSan Diego · United States73 works · 51,948 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 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksEvaluation & benchmarks · 1,220 works

Cite

Cite as Gagan Mundada, Rohan Surana, Nandhini Swaminathan, Bodhisattwa Prasad Majumder, Junda Wu, Julian McAuley and Zhouhang Xie. 2026. Evaluating Language Model Pluralism through In-the-wild Crowd Discussions. 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.1957

@inproceedings{mundada2026evaluating,
  title = {{Evaluating Language Model Pluralism through In-the-wild Crowd Discussions}},
  author = {Gagan Mundada, Rohan Surana, Nandhini Swaminathan, Bodhisattwa Prasad Majumder, Junda Wu, Julian McAuley and Zhouhang Xie},
  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.1957},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1957}
}

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