The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
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“Kirk et al. [2024] introduces PRISM Alignment Dataset that maps the sociodemographics and preferences of 1,500 participants from 75 countries to their feedback in live interactions with 21 LLMs, focusing on subjective and multicultural perspectives on controversial topics.”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Kirk et al., 2024
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Citations
Cites
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
Mohamed, S., Png, M.-T., & Isaac, W. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00405-8 · Cited by 912
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. D. L., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.06825 · Cited by 340
Power to the People? Opportunities and Challenges for Participatory AI
Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022) · Equity and Access in Algorithms Mechanisms and Optimization · doi:10.1145/3551624.3555290 · Cited by 330
LIMA: Less Is More for Alignment
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2400 · Cited by 275
OpenAssistant Conversations - Democratizing Large Language Model Alignment
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.-R., Stevens, K., Barhoum, A., Duc, N. M., Stanley, O., Nagyfi, R., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.07327 · Cited by 218
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Jaques, N., Ghandeharioun, A., Shen, J. H., Ferguson, C., Lapedriza, A., Jones, N., Gu, S., & Picard, R. (2019) · arXiv · doi:10.48550/arxiv.1907.00456 · Cited by 126
The Challenge of Value Alignment: From Fairer Algorithms to AI Safety
Gabriel, I., & Ghazavi, V. (2022) · Oxford Handbook of Digital Ethics · doi:10.1093/oxfordhb/9780198857815.013.18 · Cited by 52
STELA: a community-centred approach to norm elicitation for AI alignment
Bergman, S., Marchal, N., Mellor, J., Mohamed, S., Gabriel, I., & Isaac, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-56648-4 · Cited by 48
Cited by
A survey of personalized large language models: progress and future directions
Liu, J., Qiu, Z., Li, Z., Dai, Q., Yu, W., Zhu, J., Hu, M., Yang, M., Chua, T.-S., & King, I. (2025) · arXiv · doi:10.48550/arxiv.2502.11528 · Cited by 146
Sycophantic AI decreases prosocial intentions and promotes dependence
Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026) · Science · doi:10.1126/science.aec8352 · Cited by 185
Dahlgren Lindström, A., Methnani, L., Krause, L., Ericson, P., de Rituerto de Troya, Í. M., Coelho Mollo, D., & Dobbe, R. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09837-2 · Cited by 54
Schimmelpfennig, R., Díaz, M., Prabhakaran, V., & Davani, A. (2025) · arXiv · doi:10.48550/arxiv.2512.17898 · Cited by 2
Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
Jiang, L., Chai, Y., Li, M., Liu, M., Fok, R., Dziri, N., Tsvetkov, Y., Sap, M., Albalak, A., & Choi, Y. (2025) · arXiv · doi:10.48550/arxiv.2510.22954 · Cited by 107
Metadata
- Authors
- Kirk, H., Whitefield, A., Röttger, P., Bean, A., Margatina, K., Ciro, J., Mosquera, R., Bartolo, M., Williams, A., He, H., Vidgen, B., & Hale, S.
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 105236-105344
- Cited by
- 21
- References
- 8
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
