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README.md

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 - [ActiveRAG: Revealing the Treasures of Knowledge via Active Learning](./papers/active_rag.md) - Enhances RAG by active learning to deepen LLMs' understanding of external knowledge through innovative Knowledge Construction and Cognitive Nexus mechanisms. (Xu, Zhipeng, et al. 2024)
 
+- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](./papers/adaptive-rag.md) - The paper proposes an adaptive question-answering framework that dynamically selects the most suitable strategy for retrieval-augmented large language models based on the complexity of the query, using a classifier trained on automatically collected labels. (Jeong, Soyeong, et al. 2024)
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 ### RAG vs Finetuning
 
 - [RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture](./papers/rag_finetuning_agriculture.md) - RAG vs Fine-tuning case study on agriculture domain datasets. (Gupta, Aman, et al. 2024)

papers/adaptive-rag.md

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+# [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://arxiv.org/abs/2403.14403)
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+## Contributions
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+## Results
+
+## Insights
+
+## Resources
+- [LlamaIndex <> Mistral cookbook on Adaptive-RAG](https://github.com/mistralai/cookbook/blob/7bf0aae46ab8c763efb7800f352bcbfd8aceb8fb/third_party/LlamaIndex/Adaptive_RAG.ipynb)