Adaptive Retrieval-Augmented Generation (RAG) with Transformer-Based Dynamic Query Expansion
Keywords:
adaptive retrieval-augmented generation, dynamic query expansion, transformers, self-attentionAbstract
Advanced adaptive Retrieval-Augmented Generation (RAG) framework containing Transformer-based Dynamic Query Expansion (DQE) is used to enhance the information retrieval and knowledge synthesis. The objective of this paper is to present a model which dynamically files retrieval queries by using transformer-driven self-attention mechanisms, enabling the extraction of highly relevant contextual data prior to response generation.
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References
A. Radford et al., "Learning Transferable Visual Models From Natural Language Supervision," Proceedings of the 2021 International Conference on Machine Learning, pp. 8748-8763, 2021.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," Proceedings of NAACL-HLT, pp. 4171-4186, 2019.
A. K. Jain, R. J. Zhai, and M. S. Moens, "Transformer Models for Information Retrieval," ACM SIGIR, pp. 1125-1128, 2020.
P. Min, S. M. Yoon, and W. Lee, "Neural Retrieval Models: A Survey," ACM Computing Surveys, vol. 53, no. 4, pp. 1-33, 2021.
M. Lewis, Y. Xu, and R. Schuster, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," Proceedings of the 37th International Conference on Machine Learning, pp. 4351-4360, 2020.
D. Shen, M. Zhang, M. Song, and X. Li, "Dynamic Query Expansion Based on Transformers for Improving Document Retrieval," ACM Transactions on Information Systems, vol. 40, no. 3, pp. 1-31, 2022.
T. Kwiatkowski et al., "Natural Questions: A Benchmark for Question Answering Research," Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pp. 3709-3723, 2019.
P. Y. Tang, "Neural Network Approaches to Question Answering in the Legal Domain," Proceedings of the 2020 European Conference on Artificial Intelligence, pp. 4365-4376, 2020.
Y. Zhou, D. Yu, and Y. Xu, "Query Expansion Techniques in Information Retrieval," Information Retrieval Journal, vol. 25, no. 1, pp. 1-24, 2021.
A. Vaswani et al., "Attention is All You Need," Proceedings of NeurIPS, pp. 5998-6008, 2017.
S. Lee, K. M. Santhanam, and H. W. L. Keong, "Query Expansion for Information Retrieval: Techniques and Applications," Journal of Information Science, vol. 44, no. 2, pp. 228-242, 2018.
D. Bahdanau, K. Cho, and Y. Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate," Proceedings of ICLR, pp. 1-14, 2015.
F. Sanh, A. G. M. Vos, and D. T. J. Thorne, "DistilBERT: A Distilled Version of BERT for Smaller Models," Proceedings of NeurIPS, pp. 1-16, 2019.
Y. Xiong, S. Y. Li, and R. D. Wang, "Retrieval-Based Question Answering: Model and Datasets," Proceedings of the 2021 International Joint Conference on Artificial Intelligence, pp. 1574-1581, 2021.
S. U. Wang, "A Comprehensive Survey of Transformer Models for Natural Language Processing," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 3035-3054, 2021.
M. S. Z. W. Liang, "Transformer-Based Models in Information Retrieval: A Survey of Architectures and Applications," ACM Computing Surveys, vol. 53, no. 7, pp. 1-30, 2021.
J. Xie, H. Zeng, and W. Y. L. Zhang, "Query Expansion Techniques in Biomedical Text Mining: A Survey," Journal of Biomedical Informatics, vol. 113, pp. 103596, 2020.
K. B. Xu, Y. G. Tang, and H. W. Zeng, "Query Expansion Using Relevance Feedback for Information Retrieval in the Legal Domain," Proceedings of the 2020 International Conference on AI in Law, pp. 112-121, 2020.
M. K. Choi, K. Lee, and M. Hong, "Improving Retrieval Accuracy Through Semantic Query Expansion for Legal Information Retrieval," Journal of Artificial Intelligence in Law, vol. 24, no. 2, pp. 93-115, 2021.
H. M. Shazeer et al., "Mesh-TensorFlow: Deep Learning for Supercomputers," Proceedings of the 2020 Conference on Machine Learning and Computing, pp. 1-6, 2020.
