---
title: "“Sorry, Can't Help You” — paper by Nair et al. (2026)"
description: "Earlier studies show that LLM moral judgments change with prompt language. 44 references."
authors: ["Pavithra P M Nair", "Gilad Gressel", "Krishnashree Achuthan"]
year: 2026
venue: "Proceedings of the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP 2026)"
source: "https://aclanthology.org/2026.c3nlp-1.13/"
doi: "10.18653/v1/2026.c3nlp-1.13"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nair-2026-sorry-can-t-help-you/"
---
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# “Sorry, Can't Help You”: How Large Language Models Judge Failures to Help Across Languages

**Authors:** Pavithra P M Nair, Gilad Gressel and Krishnashree Achuthan · **Year:** 2026 · **Venue:** [Proceedings of the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP 2026)](https://md.research.elamur.ai/free/en/venues/c3nlp/) ([Workshop on Cross-Cultural Considerations in NLP 2026](https://md.research.elamur.ai/free/en/venues/c3nlp/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.c3nlp-1.13/) · [PDF](https://aclanthology.org/2026.c3nlp-1.13.pdf) · [DOI 10.18653/v1/2026.c3nlp-1.13](https://doi.org/10.18653/v1/2026.c3nlp-1.13) · [OpenAlex](https://openalex.org/W7166813358)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Earlier studies show that LLM moral judgments change with prompt language. It is unclear whether these shifts follow documented human cultural differences. The authors test this with the failure-to-help paradigm of Miller, Bersoff and Harwood (1990). That study found Indian and American adults equally disapproved of refusing help but disagreed on whether helping is an enforceable duty or a private matter.

The authors reuse the original nine experimental scenarios (three need levels × parent/friend/stranger), four control scenarios and five probe questions. They translate these with Gemini 3.1 Pro into ten languages and query four LLMs five times each. Answers are coded as Moral, Personal-moral, Personal choice or Social convention.

Prompt language significantly changes how refusals are categorized, mostly moving answers between Moral and Personal choice, while undesirability judgments stay stable. Moral labels become rarer as need gets milder and the relationship more distant. Open-weight models vary more across languages than closed ones, and the aggregate pattern resembles the American rather than the Indian sample. The authors argue that multilingual LLM evaluation should routinely include cross-lingual normative auditing.

## Key points
- **Setup:** 13 scenarios × 10 languages × 4 models × 5 iterations gave 2,600 structured JSON responses.
  - Models: GPT-5.4, Claude-Opus-4.6, DeepSeek-V3.1, Qwen3-235B.
  - Languages: Arabic, Bengali, Mandarin, English, French, Hindi, Portuguese, Russian, Spanish, Urdu.
- **Overall language effect:** χ²(27) = 116.14, p \< .001, Cramér's V = 0.147.
- **Per-model variation:**
  - Significant for Qwen3-235B (V = 0.269) and DeepSeek-V3.1 (V = 0.200, p = .002).
  - Not significant for GPT-5.4 (V = 0.168, p = .118) and Claude-Opus-4.6 (V = 0.147, p = .359).
- **South Asian languages:** Hindi, Urdu and Bengali prompts most often yielded the Personal-moral pattern. Miller et al. saw this pattern mainly among Americans, so the language-driven variation does not match the known human data.
- **Limitations and resources:**
  - Translation was machine-only with back-translation checks; a French severity anomaly was observed.
  - Human baselines exist only for English and Kannada.
  - The format is forced-choice rather than open interview.
  - Code and data are released at github.com/pavithranair/Moral-Judgments.

*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

- [Amrita Vishwa Vidyapeetham](https://md.research.elamur.ai/free/en/universities/amrita-vishwa-vidyapeetham/) — University, Coimbatore, India · 7 works · 31 citations

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## Cite

Cite as: Pavithra P M Nair, Gilad Gressel and Krishnashree Achuthan. 2026. “Sorry, Can't Help You”: How Large Language Models Judge Failures to Help Across Languages. Proceedings of the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nair2026sorry,
  title = {{“Sorry, Can't Help You”: How Large Language Models Judge Failures to Help Across Languages}},
  author = {Pavithra P M Nair, Gilad Gressel and Krishnashree Achuthan},
  booktitle = {Proceedings of the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.c3nlp-1.13},
  url = {https://doi.org/10.18653/v1/2026.c3nlp-1.13}
}
```

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40. Qwen Team. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). arXiv preprint arXiv:2505.09388.
41. Harry C Triandis. 1995. Individualism and collectivism. westview press. Boulder, CO. [find](https://scholar.google.com/scholar?q=Harry+C+Triandis%2E+1995%2E+Individualism+and+collectivism%2E+westview+press%2E+Boulder%2C+CO%2E)
42. Karina Vida, Fabian Damken, and Anne Lauscher. 2024. Decoding multilingual moral preferences: Unveiling llm’s biases through the moral machine experiment. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 7, pages 1490–1501. [find](https://scholar.google.com/scholar?q=Karina+Vida%2C+Fabian+Damken%2C+and+Anne+Lauscher%2E+2024%2E+Decoding+multilingual+moral+preferences%3A+Unveiling+llm%E2%80%99s+biases+through+the+moral+machine+experiment%2E+In+Proceedings+of+the+AAAI%2FACM+Conference+on+)
43. Wenxuan Wang, Zhaopeng Tu, Chang Chen, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, and Michael Lyu. 2024. All languages matter: On the multilingual safety of llms. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5865– 5877. {lang_name} refers to the target language int… [find](https://scholar.google.com/scholar?q=Wenxuan+Wang%2C+Zhaopeng+Tu%2C+Chang+Chen%2C+Youliang+Yuan%2C+Jen%2Dtse+Huang%2C+Wenxiang+Jiao%2C+and+Michael+Lyu%2E+2024%2E+All+languages+matter%3A+On+the+multilingual+safety+of+llms%2E+In+Findings+of+the+Association+for+)
44. A.3 Data Collection Prompt Each model was prompted using a standardized template. The prompt shown below corresponds to the English version used in the experiments. {scenario_body} denotes the full input provided to the model, including the scenario vignette, all probe questions (Q1–Q5), and their a… [find](https://scholar.google.com/scholar?q=A%2E3+Data+Collection+Prompt+Each+model+was+prompted+using+a+standardized+template%2E+The+prompt+shown+below+corresponds+to+the+English+version+used+in+the+experiments%2E+%7Bscenario%5Fbody%7D+denotes+the+full+in)

*Updated 2 November 2026*