---
title: "Who cites The Effect of Sampling — 64 citing papers"
description: "64 papers of the Elamur Research library cite «The Effect of Sampling Temperature on Problem Solving in Large Language Models», found…"
canonical: "https://research.elamur.ai/free/en/papers/renze-2024-effect-sampling-temperature-problem-solving/cited-by/"
---
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# Who cites «The Effect of Sampling Temperature on Problem Solving in Large Language Models»

64 papers of our library cite it (of the 141,840 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «The Effect of Sampling Temperature on Problem Solving in Large Language Models»](https://md.research.elamur.ai/free/en/papers/renze-2024-effect-sampling-temperature-problem-solving/)

1. [**Self-Reflection in LLM Agents: Effects on Problem-Solving Performance arXiv:2405.06682 · 2nd International Conference on Foundation and Large Language Models (FLLM 2024), pp. 476-483**Matthew Renze, Erhan Guven*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/renze-2024-self-reflection-llm-agents-effects/)
2. **[TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustness](https://md.research.elamur.ai/free/en/papers/zhou-2026-tempperturb-eval-joint-effects-internal/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yongxin Zhou, Philippe Mulhem, Didier Schwab*2026 · 1 citation*
3. **[Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?](https://md.research.elamur.ai/free/en/papers/rajput-2026-evaluation-drift-llm-personality-induction/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Prateek Kumar Rajput, Yewei Song, Iyiola Emmanuel Olatunji, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/renze-2024-effect-sampling-temperature-problem-solving/) — The Effect of Sampling Temperature on Problem Solving in Large Language Models