🔗 Who cites «Fairness-guided Few-shot Prompting for Large Language Models»

41 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Fairness-guided Few-shot Prompting for Large Language Models»

1. WizardLM: Empowering large pre-trained language models to follow complex instructionsInternational Conference on Learning Representations (ICLR)Can Xu, Qingfeng Sun, Kai Zheng, et al.2023 · 741 citations
2. The Prompt Report: A Systematic Survey of Prompt Engineering TechniquesarXiv:2406.06608Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.2024 · 189 citations
3. In-context Learning with Retrieved Demonstrations for Language Models: A SurveyarXiv:2401.11624Man Luo, Xin Xu, Yue Liu, et al.2024 · 54 citations
4. ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error DetectionFindings of the Association for Computational LinguisticsYibo Yan, Shen Wang, Jiahao Huo, et al.2026 · 23 citations
5. Automatic Combination of Sample Selection Strategies for Few-Shot LearningFindings of the Association for Computational LinguisticsBranislav Pecher, Ivan Srba, Maria Bielikova, et al.2026 · 15 citations
6. Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?Annual Meeting of the Association for Computational Linguistics (ACL)Jiayu Liu, Qing Zong, Weiqi Wang, et al.2025 · 8 citations
7. Rethinking Prompt Optimizers: From Prompt Merits to OptimizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Zixiao Zhu, Hanzhang Zhou, Zijian Feng, et al.2026 · 3 citations

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

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