--- title: "Who cites Demystifying Prompts in — 109 citing papers" description: "109 papers of the Elamur Research library cite «Demystifying Prompts in Language Models via Perplexity Estimation», found in their…" canonical: "https://research.elamur.ai/free/en/papers/gonen-2022-demystifying-prompts-language-models-perplexity/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/gonen-2022-demystifying-prompts-language-models-perplexity/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Demystifying Prompts in Language Models via…](https://md.research.elamur.ai/free/en/papers/gonen-2022-demystifying-prompts-language-models-perplexity/) / cited by # Who cites «Demystifying Prompts in Language Models via Perplexity Estimation» 109 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · [← back to «Demystifying Prompts in Language Models via Perplexity Estimation»](https://md.research.elamur.ai/free/en/papers/gonen-2022-demystifying-prompts-language-models-perplexity/) 1. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/) 2. **[Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 424 citations* 3. **[Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://md.research.elamur.ai/free/en/papers/chang-2023-speak-memory-archaeology-books-known/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kent K. Chang, Mackenzie Cramer, Sandeep Soni, et al.*2023 · 169 citations* 4. [**Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention USENIX ATC 2024**Bin Gao, Zhuomin He, Puru Sharma, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-cost-efficient-large-language-model/) 5. **[Large Language Model Instruction Following: A Survey of Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Renze Lou, Kai Zhang, Wenpeng Yin*2024 · 83 citations* 6. [**PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models arXiv:2402.07867**Wei Zou, Runpeng Geng, Binghui Wang, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/zou-2024-poisonedrag-knowledge-corruption-attacks-retrieval/) 7. **[Language models are not naysayers: An analysis of language models on negation benchmarks](https://md.research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Thinh Hung Truong, Timothy Baldwin, Karin Verspoor, et al.*2023 · 78 citations* 8. [**Instruction Mining: Instruction Data Selection for Tuning Large Language Models arXiv:2307.06290**Yihan Cao, Yanbin Kang, Chi Wang, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-instruction-mining-instruction-data-selection/) 9. **[SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://md.research.elamur.ai/free/en/papers/ye-2023-satlm-satisfiability-aided-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Qiaochu Chen, Isil Dillig, et al.*2023 · 62 citations* 10. [**In-context Learning with Retrieved Demonstrations for Language Models: A Survey arXiv:2401.11624**Man Luo, Xin Xu, Yue Liu, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/) 11. [**Pushing the Limits of ChatGPT on NLP Tasks arXiv:2306.09719**Xiaofei Sun, Linfeng Dong, Xiaoya Li, et al.*2023 · 28 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-pushing-limits-chatgpt-nlp-tasks/) 12. **[AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://md.research.elamur.ai/free/en/papers/lucy-2024-aboutme-self-descriptions-webpages-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Li Lucy, Suchin Gururangan, Luca Soldaini, et al.*2024 · 15 citations* 13. **[A Reality Check on Context Utilisation for Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/hagstrom-2025-reality-check-context-utilisation-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lovisa Hagström, Sara Vera Marjanovic, Haeun Yu, et al.*2025 · 13 citations* 14. **[CER: Confidence Enhanced Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/razghandi-2025-cer-confidence-enhanced-reasoning-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ali Razghandi, Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah*2025 · 8 citations* 15. **[Incomplete In-context Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-incomplete-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqiang Wang, Wen Yujia, Yan Xiao, et al.*2026 · 2 citations* 16. **[Prompting Instruction-tuned LLMs for Semantic Similarity Values](https://md.research.elamur.ai/free/en/papers/snelder-2026-prompting-instruction-tuned-llms-semantic/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Xander Akiko Snelder, Yunchong Huang, Jelke Bloem*2026 · 2 citations* 17. **[How Important is ‘Perfect' English for Machine Translation Prompts?](https://md.research.elamur.ai/free/en/papers/schmidtova-2026-how-important-is-perfect-english/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Patrícia Schmidtová, Niyati Bafna, Seth Aycock, et al.*2026 · 1 citation* 18. **[From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2026-fake-real-mitigating-out-distribution/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rui Song, Yingji Li, Jian Li, et al.*2026* 91 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/gonen-2022-demystifying-prompts-language-models-perplexity/) — Demystifying Prompts in Language Models via Perplexity Estimation