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The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 1901
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AFaCTA: Assisting the annotation of factual claim detection with reliable LLM annotators
Ni, J., Shi, M., Stammbach, D., Sachan, M., Ash, E., and Leippold, M. (2024) · 1912
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Web-scale information extraction in knowitall: (preliminary results)
Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S., and Yates, A. (2004) · 2004
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Duc 2005: Evaluation of question-focused summarization systems
Dang, H. T. (2006) · 2005
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Mining knowledge from text using information extraction
Mooney, R. J. and Bunescu, R. (2005) · 2005
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Su, D., Xu, Y., Yu, T., Siddique, F. B., Barezi, E. J., and Fung, P. (2020) · 2005
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Duplicate record detection: A survey
Elmagarmid, A. K., Ipeirotis, P. G., and Verykios, V. S. (2006) · 2006
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Making sense of sensemaking 1: Alternative perspectives
Klein, G., Moon, B., and Hoffman, R. R. (2006) · 2006
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Modularity and community structure in networks
Newman, M. E. (2006) · 2006
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TextRunner: Open information extraction on the web
Yates, A., Banko, M., Broadhead, M., Cafarella, M., Etzioni, O., and Soderland, S. (2007) · 2007
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Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008) · 2008
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Generation-augmented retrieval for open-domain question answering
Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., and Chen, W. (2020) · 2009
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Community detection in graphs
Fortunato, S. (2010) · 2010
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Openord: An open-source toolbox for large graph layout
Martin, S., Brown, W. M., Klavans, R., and Boyack, K. (2011) · 2011
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Scikit-learn: Machine learning in python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
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The data matching process
Christen, P. and Christen, P. (2012) · 2012
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Forceatlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software
Jacomy, M., Venturini, T., Heymann, S., and Bastian, M. (2014) · 2014
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Probabilistic knowledge graph construction: Compositional and incremental approaches
Kim, D., Xie, L., and Ong, C. S. (2016) · 2016
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Representation learning of large-scale knowledge graphs via entity feature combinations
Tan, Z., Zhao, X., and Wang, W. (2017) · 2017
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Recent advances in document summarization
Yao, J.-g., Wan, X., and Xiao, J. (2017) · 2017
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Baumel, T., Eyal, M., and Elhadad, M. (2018) · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D. (2018) · 2018
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Graspy: Graph statistics in python
Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., and Vogelstein, J. T. (2019) · 2019
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From Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., and Van Eck, N. J. (2019) · 2019
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Query focused abstractive summarization via incorporating query relevance and transfer learning with transformer models
Laskar, M. T. R., Hoque, E., and Huang, J. (2020) · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. (2020) · 2020
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Neural networks for entity matching: A survey
Barlaug, N. and Gulla, J. A. (2021) · 2021
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A survey of community detection approaches: From statistical modeling to deep learning
Jin, D., Yu, Z., Jiao, P., Pan, S., He, D., Wu, J., Philip, S. Y., and Zhang, W. (2021) · 2021
Cited alongside, same era.
Text summarization with latent queries
Xu, Y. and Lapata, M. (2021) · 2021
Cited alongside, same era.
Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp
Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., and Zaharia, M. (2022) · 2022
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · 2023
Later among the works it cites.
Enhancing knowledge graph construction using large language models
Trajanoska, M., Stojanov, R., and Trajanov, D. (2023) · 2023
Later among the works it cites.
Exploring large language models for knowledge graph completion
Yao, L., Peng, J., Mao, C., and Luo, Y. (2023) · 2023
Later among the works it cites.
Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt
Zhang, J. (2023) · 2023
Later among the works it cites.
Lift yourself up: Retrieval-augmented text generation with self-memory
Cheng, X., Luo, D., Chen, X., Liu, L., Zhao, D., and Yan, R. (2024) · 2024
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Knowledge graph generation from text
Melnyk, I., Dognin, P., and Das, P. (2022) · 2022
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Trivedi, H., Balasubramanian, N., Khot, T., and Sabharwal, A. (2022) · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D. (2022) · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. (2023) · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al. (2023) · 2023
Cited alongside, same era.
Knowledge-augmented language model prompting for zero-shot knowledge graph question answering
Baek, J., Aji, A. F., and Saffari, A. (2023) · 2023
Cited alongside, same era.
From query tools to causal architects: Harnessing large language models for advanced causal discovery from data
Ban, T., Chen, L., Wang, X., and Chen, H. (2023) · 2023
Cited alongside, same era.
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G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
He, X., Tian, Y., Sun, Y., Chawla, N. V., Laurent, T., LeCun, Y., Bresson, X., and Hooi, B. (2024) · 2024
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Evaluating large language models in theory of mind tasks
Kosinski, M. (2024) · 2024
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In search of needles in a 11m haystack: Recurrent memory finds what llms miss
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Langchain graphs
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GraphRAG Implementation with LlamaIndex - V2
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. (2024) · 2024
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Nebulagraph launches industry-first graph rag: Retrieval-augmented generation with llm based on knowledge graphs
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Get started with graphrag: Neo4j’s ecosystem tools
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Does writing with language models reduce content diversity?
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Deus ex machina and personas from large language models: Investigating the composition of ai-generated persona descriptions
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Raptor: Recursive abstractive processing for tree-organized retrieval
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Behind the Tech
Scott, K. (2024) · 2024
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Understanding human-ai workflows for generating personas
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Reflexion: Language agents with verbal reinforcement learning
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MultiHop-RAG: Benchmarking retrieval-augmented generation for multi-hop queries
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Feb4rag: Evaluating federated search in the context of retrieval augmented generation
Wang, S., Khramtsova, E., Zhuang, S., and Zuccon, G. (2024) · 2024
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Yuan, X., Li, J., Wang, D., Chen, Y., Mao, X., Huang, L., Xue, H., Wang, W., Ren, K., and Wang, J. (2024) · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al. (2024) · 2024
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Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities
Zhu, Y., Wang, X., Chen, J., Qiao, S., Ou, Y., Yao, Y., Deng, S., Chen, H., and Zhang, N. (2024) · 2024
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Towards effective extraction and evaluation of factual claims
Metropolitansky, D. and Larson, J. (2025) · 2025
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