--- title: "Who cites LLM-Blender: Ensembling Large — 178 citing papers" description: "178 papers of the Elamur Research library cite «LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative…" canonical: "https://research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LLM-Blender: Ensembling Large Language Models…](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/) / cited by # Who cites «LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion» 178 papers of our library cite it (of the 141,840 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion»](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/) 1. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling arXiv:2407.21787**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 241 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/) 2. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models arXiv:2405.01535**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 220 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/) 3. **[InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://md.research.elamur.ai/free/en/papers/zhan-2024-injecagent-benchmarking-indirect-prompt-injections/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiusi Zhan, Zhixiang Liang, Zifan Ying, et al.*2024 · 187 citations* 4. **[ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 154 citations* 5. [**LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks arXiv:2406.18403**Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/) 6. [**Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs arXiv:2410.18451**Chris Yuhao Liu, Liang Zeng, Jiacai Liu, et al.*2024 · 104 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-skywork-reward-bag-tricks/) 7. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/) 8. **[Mitigating the Alignment Tax of RLHF](https://md.research.elamur.ai/free/en/papers/lin-2024-mitigating-alignment-tax-rlhf/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yong Lin, Hangyu Lin, Wei Xiong, et al.*2024 · 82 citations* 9. [**Inference-Time Scaling for Generalist Reward Modeling arXiv:2504.02495**Zijun Liu, Peiyi Wang, Runxin Xu, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-inference-time-scaling-generalist/) 10. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/) 11. **[A Binary Problem in Binary QA: Diverse LLMs or Diverse Question Interpretations? That Is the Ensembling Question](https://md.research.elamur.ai/free/en/papers/rosales-2026-binary-problem-binary-qa-diverse/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rafael Rosales, Santiago Miret*2026* 12. **[A Single Model Ensemble Framework for Neural Machine Translation Using Pivot Translation](https://md.research.elamur.ai/free/en/papers/oh-2026-single-model-ensemble-framework-neural/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Seokjin Oh, Keonwoong Noh, Woohwan Jung*2026* 13. **[Multilingual KokoroChat: A Multi-LLM Ensemble Translation Method for Creating a Multilingual Counseling Dialogue Dataset](https://md.research.elamur.ai/free/en/papers/suzuki-2026-multilingual-kokorochat-multi-llm-ensemble/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ryoma Suzuki, Zhiyang Qi, Michimasa Inaba*2026* 165 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/) — LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion