---
title: "Who cites “According to . . . “: Prompting — 41 citing papers"
description: "41 papers of the Elamur Research library cite «“According to. Among them: LiveCodeBench: Holistic and Contamination Free Evaluation of…"
canonical: "https://research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [“According to . . . “: Prompting Language Models…](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) / cited by

# Who cites «“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data»

41 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data»](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/)

1. [**LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code arXiv:2403.07974**Naman Jain, King Han, Alex Gu, et al.*2024 · 578 citations*](https://md.research.elamur.ai/free/en/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/)
2. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
3. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
4. [**Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers arXiv:2409.04109**Chenglei Si, Diyi Yang, Tatsunori Hashimoto*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/)
5. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
6. [**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/)
7. **[HALoGEN: Fantastic LLM Hallucinations and Where to Find Them](https://md.research.elamur.ai/free/en/papers/ravichander-2025-halogen-fantastic-llm-hallucinations-where/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abhilasha Ravichander, Shrusti Ghela, David Wadden, et al.*2025 · 22 citations*
8. [**Source-Aware Training Enables Knowledge Attribution in Language Models arXiv:2404.01019**Muhammad Khalifa, David Wadden, Emma Strubell, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/khalifa-2024-source-aware-training-enables-knowledge/)
9. **[DebateQA: Evaluating Question Answering on Debatable Knowledge](https://md.research.elamur.ai/free/en/papers/xu-2026-debateqa-evaluating-question-answering-debatable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rongwu Xu, Xuan Qi, Zehan Qi, et al.*2026 · 7 citations*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) — “According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data