--- title: "Who cites Exploring the Benefits of Training — 44 citing papers" description: "44 papers of the Elamur Research library cite «Exploring the Benefits of Training Expert Language Models over Instruction Tuning», found in their…" canonical: "https://research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Exploring the Benefits of Training Expert…](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/) / cited by # Who cites «Exploring the Benefits of Training Expert Language Models over Instruction Tuning» 44 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Exploring the Benefits of Training Expert Language Models over Instruction Tuning»](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/) 1. [**TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation arXiv:2305.00447**Keqin Bao; Jizhi Zhang; Yang Zhang; Wenjie Wang; Fuli Feng;, Xiangnan He*2023 · 531 citations*](https://md.research.elamur.ai/free/en/papers/bao-2023-tallrec-effective-efficient-tuning-framework/) 2. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) 3. [**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 · 234 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/) 4. **[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://md.research.elamur.ai/free/en/papers/kim-2023-cot-collection-improving-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungone Kim, Se Joo, Doyoung Kim, et al.*2023 · 118 citations* 5. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/) 6. [**Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging arXiv:2310.11564**Joel Jang, Seungone Kim, Bill Yuchen Lin, et al.*2023 · 102 citations*](https://md.research.elamur.ai/free/en/papers/jang-2023-personalized-soups-personalized-large-language/) 7. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/) 8. **[Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://md.research.elamur.ai/free/en/papers/rame-2023-rewarded-soups-towards-pareto-optimal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.*2023 · 90 citations* 9. [**Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities arXiv:2408.07666**Enneng Yang, Li Shen, Guibing Guo, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-model-merging-llms-mllms-methods/) 10. **[Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models](https://md.research.elamur.ai/free/en/papers/blevins-2024-breaking-curse-multilinguality-cross-lingual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Terra Blevins, Tomasz Limisiewicz, Suchin Gururangan, et al.*2024 · 42 citations* 11. **[Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts](https://md.research.elamur.ai/free/en/papers/tan-2024-personalized-pieces-efficient-personalized-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaoxuan Tan, Zheyuan Liu, Meng Jiang*2024 · 40 citations* 12. **[Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/doddapaneni-2025-cross-lingual-auto-evaluation-assessing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh, et al.*2025 · 11 citations* 13. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations* 31 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/) — Exploring the Benefits of Training Expert Language Models over Instruction Tuning