The DOI matches its Crossref record; 111 of the 112 references it cites were found in a registry or an index.
Societal Biases in Language Generation: Progress and Challenges
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Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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“LLMs have been found to internalize, spread, and potentially magnify harmful information existing in the crawled training corpora, usually, toxic languages, like offensiveness, hate speech, and insults [53], as well as social biases like stereotypes towards people with a particular demographic identity ( e.g. , gender, race, religion, occupation, and ideology) [175].”
From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [175]
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Metadata
- Authors
- Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N.
- Affiliations
- University of Southern California (US)
- University of California, Los Angeles (US)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 4275-4293
- OpenAlex
- W3174685870
- Cited by
- 131
- References
- 99
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 18 · 2023: 43 · 2022: 16 · 2021: 8
- FWCI
- 19.25
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- language generation, algorithmic fairness, algorithmic bias, natural language generation
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