🔗 Who cites «An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models»

1. Llama 2: Open Foundation and Fine-Tuned Chat ModelsarXiv:2307.09288Hugo Touvron, Louis Martin, Kevin Stone, et al.2023 · 9,669 citations
2. Textbooks Are All You Need II: phi-1.5 technical reportarXiv:2309.05463Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, et al.2023 · 336 citations
3. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
4. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
5. SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language ModelsFindings of the Association for Computational LinguisticsLijun Li, Bowen Dong, Ruohui Wang, et al.2024 · 119 citations
6. Measuring Political Bias in Large Language Models: What Is Said and How It Is SaidAnnual Meeting of the Association for Computational Linguistics (ACL)Yejin Bang, Delong Chen, Nayeon Lee, et al.2024 · 76 citations
7. Airavata: Introducing Hindi Instruction-tuned LLMarXiv:2401.15006Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain, et al.2024 · 31 citations
8. Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)David Guzman Piedrahita, Irene Strauss, Rada Mihalcea, et al.2026 · 3 citations

8 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/hosseini-2023-empirical-study-metrics-measure-representational/cited-by/