---
title: "Who cites Peter Hase — 388 papers of our library"
description: "388 papers of the Elamur Research library cite the works of Peter Hase, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/peter-hase/cited-by/"
---
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# Who cites Peter Hase

403 citations of their works from 388 papers of our library (of the 141,840 whose bibliographies we read) · 27 with a page here, the most cited first · [← Peter Hase](https://md.research.elamur.ai/free/en/authors/peter-hase/)

1. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,056 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
2. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,863 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
3. [**GLM-130B: An Open Bilingual Pre-trained Model**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 779 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
4. [**Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 568 citations*](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/)
5. [**Do Prompt-Based Models Really Understand the Meaning of Their Prompts?**Albert Webson, Ellie Pavlick*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/)
6. [**Automatic Prompt Optimization with “Gradient Descent” and Beam Search**Reid Pryzant, Dan Iter, Jerry Li, et al.*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/pryzant-2023-automatic-prompt-optimization-gradient-descent/)
7. [**Large Language Models as Optimizers**Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.*2023 · 390 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/)
8. [**RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/)
9. [**Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 272 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/)
10. [**Ignore Previous Prompt: Attack Techniques For Language Models**Fabio Perez, Ian Ribeiro*2022 · 245 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-ignore-previous-prompt-attack-techniques/)
11. [**Measuring Association Between Labels and Free-Text Rationales**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 215 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/)
12. [**Towards Faithful Model Explanation in NLP: A Survey**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/)
13. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 187 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
14. [**Knowledge Conflicts for LLMs: A Survey**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 179 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)
15. [**Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 174 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/)
16. [**Quark: Controllable Text Generation with Reinforced Unlearning**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 165 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/)
17. [**Language Models as Agent Models**Jacob Andreas*2022 · 157 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/)
18. [**SCOTT: Self-Consistent Chain-of-Thought Distillation**Peifeng Wang, Zhengyang Wang, Zheng Li, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-scott-self-consistent-chain-thought/)
19. [**Prompting is not a substitute for probability measurements in large language models**Jennifer Hu, Roger Levy*2023 · 133 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/)
20. [**Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data**Kashun Shum, Shizhe Diao, Tong Zhang*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/shum-2023-automatic-prompt-augmentation-selection-chain/)
21. [**Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?**Peter Hase, Shiyue Zhang, Harry Xie, et al.*2020 · 120 citations*](https://md.research.elamur.ai/free/en/papers/hase-2020-leakage-adjusted-simulatability-can-models/)
22. [**Aligning Faithful Interpretations with their Social Attribution**Alon Jacovi, Yoav Goldberg*2021 · 119 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2021-aligning-faithful-interpretations-their-social/)
23. [**Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?**Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, et al.*2022 · 107 citations*](https://md.research.elamur.ai/free/en/papers/pruthi-2022-evaluating-explanations-how-much-do/)
24. [**PRover: Proof Generation for Interpretable Reasoning over Rules**Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, et al.*2020 · 101 citations*](https://md.research.elamur.ai/free/en/papers/saha-2020-prover-proof-generation-interpretable-reasoning/)
25. [**Faithfulness Tests for Natural Language Explanations**Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/atanasova-2023-faithfulness-tests-natural-language-explanations/)
26. [**Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning**Debjit Paul, Robert West, Antoine Bosselut, et al.*2024 · 77 citations*](https://md.research.elamur.ai/free/en/papers/paul-2024-making-reasoning-matter-measuring-improving/)
27. [**Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning**Ming Li, Yong Zhang, Shwai He, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/)

361 more citing papers of our library have no page here yet.

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