---
title: "Who cites Me LLaMA: Foundation Large — 25 citing papers"
description: "25 papers of the Elamur Research library cite «Me LLaMA: Foundation Large Language Models for Medical Applications», found in their…"
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---
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# Who cites «Me LLaMA: Foundation Large Language Models for Medical Applications»

25 papers of our library cite it (of the 149,439 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «Me LLaMA: Foundation Large Language Models for Medical Applications»](https://md.research.elamur.ai/free/en/papers/xie-2024-me-llama-foundation-large-language/)

1. [**Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks arXiv:2404.00376**Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-small-language-models-learn-enhanced/)
2. [**OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs arXiv:2411.14199**Akari Asai, Jacqueline He, Rulin Shao, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/asai-2024-openscholar-synthesizing-scientific-literature/)
3. **[AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-agenticeval-agentic-self-evolving-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixu Wang, Xin Wang, Yang Yao, et al.*2026 · 1 citation*
4. **[From Narrow Unlearning to Emergent Misalignment in LLMs](https://md.research.elamur.ai/free/en/papers/mushtaq-2026-narrow-unlearning-emergent-misalignment-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Erum Mushtaq, Anil Ramakrishna, Satyapriya Krishna, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/xie-2024-me-llama-foundation-large-language/) — Me LLaMA: Foundation Large Language Models for Medical Applications
