---
title: "Who cites Joint Prompt Optimization of — 18 citing papers"
description: "18 papers of the Elamur Research library cite «Joint Prompt Optimization of Stacked LLMs using Variational Inference», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/sordoni-2023-joint-prompt-optimization-stacked-llms/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sordoni-2023-joint-prompt-optimization-stacked-llms/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Joint Prompt Optimization of Stacked LLMs using…](https://md.research.elamur.ai/free/en/papers/sordoni-2023-joint-prompt-optimization-stacked-llms/) / cited by

# Who cites «Joint Prompt Optimization of Stacked LLMs using Variational Inference»

18 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «Joint Prompt Optimization of Stacked LLMs using Variational Inference»](https://md.research.elamur.ai/free/en/papers/sordoni-2023-joint-prompt-optimization-stacked-llms/)

1. [**Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models arXiv:2312.06585**Avi Singh, John D. Co-Reyes, Rishabh Agarwal, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/singh-2023-human-data-scaling-self-training/)
2. **[Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://md.research.elamur.ai/free/en/papers/ong-2024-optimizing-instructions-demonstrations-multi-stage/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, et al.*2024 · 99 citations*
3. [**Wider and Deeper LLM Networks are Fairer LLM Evaluators arXiv:2308.01862**Xinghua Zhang, Bowen Yu, Haiyang Yu, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-wider-deeper-llm-networks-fairer/)
4. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations*
5. [**Symbolic Learning Enables Self-Evolving Agents arXiv:2406.18532**Wangchunshu Zhou, Yixin Ou, Shengwei Ding, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-symbolic-learning-enables-self-evolving/)
6. **[Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models](https://md.research.elamur.ai/free/en/papers/xiong-2025-beyond-outlining-heterogeneous-recursive-planning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruibin Xiong, Yimeng Chen, Dmitrii Khizbullin, et al.*2025 · 8 citations*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/sordoni-2023-joint-prompt-optimization-stacked-llms/) — Joint Prompt Optimization of Stacked LLMs using Variational Inference