llm-lab/alignment-veto

The Alignment Veto: questions and human answers

Questions and human survey answers from The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs (EMNLP 2026).

Paper · Website · Code · Model responses (gated)

Files

File	Rows	What it is
questions.csv	864 (all)	question_id, source (wvs_wave7 or arab_opinion_index), tier (1 benign · 2 moderate · 3 sensitive), question_text, scale_min, scale_max
human_answers.csv	one per question × country, WVS-sourced questions only	question_id, country, language, human_mean, distribution (JSON: answer option → share of respondents)
tiers_wvs_only.csv, new_weights_transposed_wvs_only.xlsx		the original-format files, filtered to WVS questions, so the GitHub code runs on this subset unchanged

from datasets import load_dataset
questions = load_dataset("llm-lab/alignment-veto", "questions", split="train")       # all 864
human     = load_dataset("llm-lab/alignment-veto", "human_answers", split="train")   # WVS subset

The 864 questions come from two surveys: 291 from World Values Survey (WVS) Wave 7, and 573 from the Arab Opinion Index (AOI). All 864 question texts are published in questions.csv — the source column tells you which survey each one is from.

For the human answer data (country-level means and response distributions), WVS's terms clearly permit redistributing aggregates, so human_answers.csv here covers all 291 WVS questions across the 9 countries WVS Wave 7 covers for this set (Egypt, Iran, Iraq, Jordan, Lebanon, Libya, Morocco, Tunisia, Turkey).

We have not yet confirmed that the Arab Opinion Index's terms permit redistributing aggregates, so the AOI-sourced human answers (573 questions, all 16 countries) and their native-language (Arabic) translations are not included here. If you need them for research, email the corresponding author (zahraei2@illinois.edu).

Sources

Human answers are country-level aggregates from the World Values Survey Wave 7 and the Arab Opinion Index. If you use them, please cite those surveys as well as our paper.

Citation

@inproceedings{zahraei2026alignmentveto,
  title     = {The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs},
  author    = {Zahraei, Pardis Sadat and Tur, Gokhan and Hakkani-T{\"u}r, Dilek and Asgari, Ehsaneddin},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026},
  publisher = {Association for Computational Linguistics},
  url       = {https://arxiv.org/abs/2510.13154}
}