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The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models

Paper · 2026 · Giuseppe Russo, Debora Nozza, Paul Röttger et al.

Summary

People increasingly ask LLMs for moral advice, but earlier evaluations mostly check agreement with the majority verdict. They also tend to use synthetic dilemmas. The authors instead ask whether models reproduce the spread of human opinion. They build the Moral Dilemma Dataset (MDD) from Reddit's r/AmITheAsshole. Each case pairs a rewritten real-life dilemma with many human verdicts (binary judgment plus rationale). The rewrites were generated with GPT-4o-mini and then filtered to reduce sycophancy and memorization risks.

Ten LLMs were tested with zero-shot, persona-based, and council-of-models prompting. They match humans only when people largely agree, and they diverge as human disagreement grows. A 60-value taxonomy, derived via Value Kaleidoscope, shows that model rationales concentrate on far fewer values than human ones. The authors call this mismatch the pluralistic moral gap.

Their remedy is Dynamic Moral Profiling (DMP). It samples human-derived, topic-specific value profiles from a Dirichlet distribution and conditions generation on them. This improves alignment and value diversity. The work matters because contested cases are exactly where advice is most sought.

Key points

  • Dataset construction: MDD starts from 22,451 AITA posts (July–December 2024) and 684,360 direct replies. After rewriting and memorization filtering, only 7.2% of dilemmas survived. The final set has 1,618 dilemmas and 51,776 judgments, and only the NTA and YTA labels are kept.
  • Rewrite validation: A two-group human study confirmed that the rewrites preserve meaning, with 86–92% agreement on key narrative elements.
  • Models and metric: Ten models from the GPT, Claude, Mistral, LLaMA, DBRX, and Qwen families were evaluated. The measure is the absolute gap in the share of "acceptable" verdicts, analyzed across consensus buckets ranging from 0.5 to 1.0.
  • Narrow value use: The value taxonomy (60 values from 3,783 extracted expressions) shows that the ten values LLMs cite most often account for 81.6% of everything they cite, compared with 35.2% for humans.
  • DMP results: DMP raises alignment with human values by 64.3% and overall value diversity by 13.1%. A noted limitation is that the data reflect Western, English-speaking norms.

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 OxfordOxford · United Kingdom104 works · 157,183 citations🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland44 works · 7,050 citations🎓Bocconi UniversityMilan · Italy28 works · 3,077 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Social Sciences · 1,772 worksSafety Research · 543 worksDecision Sciences · 209 worksNeuroscience · 165 worksCognitive Neuroscience · 164 worksInformation Systems and Management · 41 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Giuseppe Russo, Debora Nozza, Paul Röttger and Dirk Hovy. 2026. The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models. Proceedings of the 19th Conference of the European Chapter 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.eacl-long.305

@inproceedings{russo2026pluralistic,
  title = {{The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models}},
  author = {Giuseppe Russo, Debora Nozza, Paul Röttger and Dirk Hovy},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.305},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.305}
}

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