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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)
+
 ### 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)
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+# [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://arxiv.org/abs/2403.14403)
+> This paper card is generated with Claude 3 Opus
+
+## Contributions
+- Proposes Adaptive-RAG, a framework that dynamically adjusts retrieval-augmented LLM strategies based on query complexity, spanning from non-retrieval for straightforward queries to multi-step approaches for complex queries.
+- Introduces a classifier to determine query complexity, which is trained on automatically collected data from model prediction outcomes and dataset biases.
+
+## Results
+- Adaptive-RAG significantly improves overall accuracy and efficiency compared to existing one-size-fits-all approaches on a collection of open-domain QA datasets covering diverse query complexities.
+- The classifier effectively categorizes queries into different complexity levels, contributing to the selection of the most suitable retrieval-augmented LLM strategy.
+
+## Insights
+- Real-world queries have varying complexities, and existing retrieval-augmented generation approaches tend to be overly simple or complex.
+- Adapting retrieval-augmented LLMs to the assessed query complexity enables the utilization of the most suitable approach tailored to each query.
+- The query complexity classifier plays a crucial role in the adaptive framework, and there is still room for improvement in its architecture and training data.
+
+## Resources
+- [LlamaIndex <> Mistral cookbook on Adaptive-RAG](https://github.com/mistralai/cookbook/blob/7bf0aae46ab8c763efb7800f352bcbfd8aceb8fb/third_party/LlamaIndex/Adaptive_RAG.ipynb)
+- Official Code: https://github.com/starsuzi/Adaptive-RAG
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