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Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources.
SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , Jian Su, Kevin Duh, and Xavier Carreras (Eds.). Association for Computational Linguistics, Austin, Texas, 2383–2392
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Conference on Empirical Methods in Natural Language Processing (EMNLP)
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Earlier work this paper cites.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023
Earlier work this paper cites.
Efficiently scaling transformer inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean. 2023 · 2023
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Cited alongside, same era.
RULER: What’s the Real Context Size of Your Long-Context Language Models?. In First Conference on Language Modeling
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg. 2024 · 2024
Cited alongside, same era.
Long-context LLMs Struggle with Long In-context Learning
Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, and Wenhu Chen. 2024b
Cited in the paper.
Long Context RAG Performance of Large Language Models
Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, and Michael Carbin. 2024 · 2024
Closest in time.
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track . Association for Computational Linguistics, Miami, Florida, US, 881–893
Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024a · 2024
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TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text
Songshuo Lu, Hua Wang, Yutian Rong, Zhi Chen, and Yaohua Tang. 2024 · 2024
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Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. [n. d.]
Cited in the paper.