Conclusion, Limitations, Ethics Statement, Acknowledgements, and References

Table of Links

Abstract and 1. Introduction

2 Related Work

3 Method and 3.1 Preliminaries

3.2 Adaptive-RAG: Adaptive Retrieval-Augmented Generation

4 Experimental Setups and 4.1 Datasets

4.2 Models and 4.3 Evaluation Metrics

4.4 Implementation Details

5 Experimental Results and Analyses

6 Conclusion, Limitations, Ethics Statement, Acknowledgements, and References

A Additional Experimental Setups

B Additional Experimental Results

6 Conclusion

In this work, we proposed the Adaptive RetrievalAugmented Generation framework, referred to as Adaptive-RAG, to handle queries of various complexities. Specifically, Adaptive-RAG is designed to dynamically adjust its query handling strategies in the unified retrieval-augmented LLM based on the complexity of queries that they encounter, which spans across a spectrum of the nonretrieval-based approach for the most straightforward queries, to the single-step approach for the queries of moderate complexity, and finally to the multi-step approach for the complex queries. The core step of our Adaptive-RAG lies in determining the complexity of the given query, which is instrumental in selecting the most suitable strategy for its answer. To operationalize this process, we trained a smaller Language Model with querycomplexity pairs, which are automatically annotated from the predicted outcomes and the inductive biases in datasets. We validated our Adaptive-RAG on a collection of open-domain QA datasets, covering the multiple query complexities including both the single- and multi-hop questions. The results demonstrate that our Adaptive-RAG enhances the overall accuracy and efficiency of QA systems, allocating more resources to handle complex queries while efficiently handling simpler queries, compared to the existing one-size-fits-all approaches that tend to be either minimalist or maximalist over varying query complexities.

Limitations

While our Adaptive-RAG shows clear advantages in effectiveness and efficiency by determining the query complexity and then leveraging the most suitable approach for tackling it, it is important to recognize that there still exist potential avenues for improving the classifier from the perspectives of its training datasets and architecture. Specifically, as there are no available datasets for training the query-complexity classifier, we automatically create new data based on the model prediction outcomes and the inductive dataset biases. However, our labeling process is one specific instantiation of labeling the query complexity, and it may have the potential to label queries incorrectly despite its effectiveness. Therefore, future work may create new datasets that are annotated with a diverse range of query complexities, in addition to the labels of question-answer pairs. Also, as the performance gap between the ideal classifier in Table 1 and the current classifier in Figure 3 indicates, there is still room to improve the effectiveness of the classifier. In other words, our classifier design based on the smaller LM is the initial, simplest instantiation for classifying the query complexity, and based upon it, future work may improve the classifier architecture and its performance, which will positively contribute to the overall QA performance.

Ethics Statement

The experimental results on Adaptive-RAG validate its applicability in realistic scenarios, where a wide range of diverse user queries exist. Nonetheless, given the potential diversity of real-world user inputs, it is crucial to also consider scenarios where these inputs might be offensive or harmful. We should be aware that such inputs could lead to the retrieval of offensive documents and the generation of inappropriate responses by the retrieval augmented LLMs. To address this challenge, developing methods to detect and manage offensive or inappropriate content in both user inputs and retrieved documents within the retrieval-augmented framework is essential. We believe that this is a critical area for future work.

Acknowledgements

This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (No. 2018-0-00582, Prediction and augmentation of the credibility distribution via linguistic analysis and automated evidence document collection), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00275747), and the Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT (MSIT, Korea) & Gwangju Metropolitan City.

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