---
title: "Who cites An Empirical Study of Metrics to — 16 citing papers"
description: "16 papers of the Elamur Research library cite «An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language…"
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---
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# 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»](https://md.research.elamur.ai/free/en/papers/hosseini-2023-empirical-study-metrics-measure-representational/)

1. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
2. [**Textbooks Are All You Need II: phi-1.5 technical report arXiv:2309.05463**Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 336 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-textbooks-all-need-ii-phi/)
3. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
4. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
5. **[SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-salad-bench-hierarchical-comprehensive-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lijun 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 Said](https://md.research.elamur.ai/free/en/papers/bang-2024-measuring-political-bias-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yejin Bang, Delong Chen, Nayeon Lee, et al.*2024 · 76 citations*
7. [**Airavata: Introducing Hindi Instruction-tuned LLM arXiv:2401.15006**Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/gala-2024-airavata-introducing-hindi-instruction-tuned/)
8. **[Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models](https://md.research.elamur.ai/free/en/papers/piedrahita-2026-democratic-or-authoritarian-probing-new/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/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.

- [← back to](https://md.research.elamur.ai/free/en/papers/hosseini-2023-empirical-study-metrics-measure-representational/) — An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models