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Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking, which aims to improve retrieval performance by dividing documents into semantically coherent segments.
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
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Natural questions: a benchmark for question answering research
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Mpnet: Masked and permuted pre-training for language understanding
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A dataset of information-seeking questions and answers anchored in research papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A Smith, and Matt Gardner. 2021 · 2021
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Conditionalqa: A complex reading comprehension dataset with conditional answers
Haitian Sun, William W Cohen, and Ruslan Salakhutdinov. 2021 · 2021
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Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models
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Mteb: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. 2022 · 2022
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Miracl: A multilingual retrieval dataset covering 18 diverse languages
Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, and Jimmy Lin. 2023 · 2023
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Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024 · 2024
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English • spacy models documentation
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The chronicles of rag: The retriever, the chunk and the generator
Paulo Finardi, Leonardo Avila, Rodrigo Castaldoni, Pedro Gengo, Celio Larcher, Marcos Piau, Pablo Costa, and Vinicius Caridá. 2024 · 2024
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Ragbench: Explainable benchmark for retrieval-augmented generation systems
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Valeriia Bolotova-Baranova, Vladislav Blinov, Sofya Filippova, Falk Scholer, and Mark Sanderson. 2023 · 2023
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Dense x retrieval: What retrieval granularity should we use?
Tong Chen, Hongwei Wang, Sihao Chen, Wenhao Yu, Kaixin Ma, Xinran Zhao, Dong Yu, and Hongming Zhang. 2023 · 2023
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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
Cited alongside, same era.
Expertqa: Expert-curated questions and attributed answers
Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber, Mark Yatskar, and Dan Roth. 2023 · 2023
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Delucionqa: Detecting hallucinations in domain-specific question answering
Mobashir Sadat, Zhengyu Zhou, Lukas Lange, Jun Araki, Arsalan Gundroo, Bingqing Wang, Rakesh R Menon, Md Rizwan Parvez, and Zhe Feng. 2023 · 2023
Cited alongside, same era.
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Chain-of-note: Enhancing robustness in retrieval-augmented language models
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023 · 2023
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Robert Friel, Masha Belyi, and Atindriyo Sanyal. 2024 · 2024
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5 levels of text splitting
Greg Kamradt. 2024 · 2024
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How to split text based on semantic similarity
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Semantic chunker
LlamaIndex. 2024 · 2024
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Solving the out-of-context chunk problem for rag
Zach McCormick. 2024 · 2024
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Hitesh Wadhwa, Rahul Seetharaman, Somyaa Aggarwal, Reshmi Ghosh, Samyadeep Basu, Soundararajan Srinivasan, Wenlong Zhao, Shreyas Chaudhari, and Ehsan Aghazadeh. 2024 · 2024
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Fanoutqa: a multi-hop, multi-document question answering benchmark for large language models
Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch. 2024 · 2024
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