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Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains.
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023) · 1940
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Probabilistic models in information retrieval
Fuhr, N. (1992) · 1992
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The anatomy of a large-scale hypertextual web search engine
Brin, S. and Page, L. (1998) · 1998
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The pagerank citation ranking: Bringing order to the web
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999) · 1999
Earlier work this paper cites.
Towards context-based search engine selection
Leake, D. B. and Scherle, R. (2001) · 2001
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A taxonomy of web search
Broder, A. (2002) · 2002
Earlier work this paper cites.
Google scholar: The new generation of citation indexes
Noruzi, A. (2005) · 2005
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The pollution effect: Optimizing keyword auctions by favoring relevant advertising
Linden, G., Meek, C., Chickering, M., and Meek, C. (2009) · 2009
Earlier work this paper cites.
The probabilistic relevance framework: Bm25 and beyond
Robertson, S., Zaragoza, H., et al. (2009) · 2009
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J. (2017) · 2017
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Clinvar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · 2018
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Uniprot: the universal protein knowledgebase
UniProt Consortium, T. (2018) · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018) · 2018
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Wikiqa: A challenge dataset for open-domain question answering
Yang, Y., Yih, W.-t., and Meek, C. (2015) · 2018
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Open-domain question answering
Chen, D. and Yih, W.-t. (2020) · 2020
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Web scraping or web crawling: State of art, techniques, approaches and application
Khder, M. A. (2021) · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P. (2021) · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. (2021) · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. (2022) · 2022
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Dungeons and dragons as a dialog challenge for artificial intelligence
Callison-Burch, C., Tomar, G. S., Martin, L. J., Ippolito, D., Bailis, S., and Reitter, D. (2022) · 2022
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Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Iyer, S., Lin, X. V., Pasunuru, R., Mihaylov, T., Simig, D., Yu, P., Shuster, K., Wang, T., Liu, Q., Koura, P. S., et al. (2022) · 2022
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Human-centered AI
Shneiderman, B. (2022) · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022) · 2022
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Glm-130b: An open bilingual pre-trained model
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Xu, Y., Zheng, W., Xia, X., et al. (2022) · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · 2022
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Large language models are human-level prompt engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J. (2022) · 2022
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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
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Social search: Retrieving information in online social platforms–a survey
Amendola, M., Passarella, A., and Perego, R. (2023) · 2023
Cited alongside, same era.
Self-rag: Learning to retrieve, generate, and critique through self-reflection
Asai, A., Wu, Z., Wang, Y., Sil, A., and Hajishirzi, H. (2023) · 2023
Cited alongside, same era.
Can llm-generated misinformation be detected?
Chen, C. and Shu, K. (2023) · 2023
Cited alongside, same era.
Reasoning with language model is planning with world model
Hao, S., Gu, Y., Ma, H., Hong, J., Wang, Z., Wang, D., and Hu, Z. (2023) · 2023
Cited alongside, same era.
Active retrieval augmented generation
Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. (2023) · 2023
Cited alongside, same era.
Chatgpt for good? on opportunities and challenges of large language models for education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · 2023
A survey of conversational search
Mo, F., Mao, K., Zhao, Z., Qian, H., Chen, H., Cheng, Y., Li, X., Zhu, Y., Dou, Z., and Nie, J.-Y. (2024) · 2024
Later among the works it cites.
Deploying large language models with retrieval augmented generation
Prabhune, S. and Berndt, D. J. (2024) · 2024
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System for systematic literature review using multiple ai agents: Concept and an empirical evaluation
Sami, A. M., Rasheed, Z., Kemell, K.-K., Waseem, M., Kilamo, T., Saari, M., Duc, A. N., Systä, K., and Abrahamsson, P. (2024) · 2024
Later among the works it cites.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A. (2024) · 2024
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Memorybank: Enhancing large language models with long-term memory
Zhong, W., Guo, L., Gao, Q., Ye, H., and Wang, Y. (2024) · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Summary of chatgpt-related research and perspective towards the future of large language models
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., Wu, Z., Zhao, L., Zhu, D., Li, X., Qiang, N., Shen, D., Liu, T., and Ge, B. (2023) · 2023
Cited alongside, same era.
Query rewriting in retrieval-augmented large language models
Ma, X., Gong, Y., He, P., Zhao, H., and Duan, N. (2023) · 2023
Cited alongside, same era.
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. (2023) · 2023
Cited alongside, same era.
Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., et al. (2023) · 2023
Cited alongside, same era.
Measuring and narrowing the compositionality gap in language models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., and Lewis, M. (2023) · 2023
Cited alongside, same era.
Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T. (2023) · 2023
Cited alongside, same era.
Inters: Unlocking the power of large language models in search with instruction tuning
Zhu, Y., Zhang, P., Zhang, C., Chen, Y., Xie, B., Liu, Z., Wen, J.-R., and Dou, Z. (2024) · 2024
Later among the works it cites.
Gptswarm: Language agents as optimizable graphs
Zhuge, M., Wang, W., Kirsch, L., Faccio, F., Khizbullin, D., and Schmidhuber, J. (2024) · 2024
Later among the works it cites.
Ask in any modality: A comprehensive survey on multimodal retrieval-augmented generation
Abootorabi, M. M., Zobeiri, A., Dehghani, M., Mohammadkhani, M., Mohammadi, B., Ghahroodi, O., Baghshah, M. S., and Asgari, E. (2025) · 2025
Closest in time.
Open deep search: Democratizing search with open-source reasoning agents
Alzubi, S., Brooks, C., Chiniya, P., Contente, E., von Gerlach, C., Irwin, L., Jiang, Y., Kaz, A., Nguyen, W., Oh, S., et al. (2025) · 2025
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Why reasoning matters? a survey of advancements in multimodal reasoning (v1)
Bi, J., Liang, S., Zhou, X., Liu, P., Guo, J., Tang, Y., Song, L., Huang, C., Sun, G., He, J., et al. (2025) · 2025
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A survey on knowledge-oriented retrieval-augmented generation
Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., Yu, S., Zhang, B., Cao, J., Ma, J., et al. (2025) · 2025
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Ensembl 2025
Dyer, S. C., Austine-Orimoloye, O., Azov, A. G., Barba, M., Barnes, I., Barrera-Enriquez, V. P., Becker, A., Bennett, R., Beracochea, M., Berry, A., et al. (2025) · 2025
Closest in time.
Scaling laws for many-shot in-context learning with self-generated annotations
Gu, Z., Zou, H. P., Chen, Y., Liu, A., Zhang, W., and Yu, P. S. (2025) · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al. (2025) · 2025
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Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Jin, B., Zeng, H., Yue, Z., Wang, D., Zamani, H., and Han, J. (2025) · 2025
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Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., and Hashimoto, T. (2025) · 2025
Closest in time.
Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al. (2025) · 2025
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Agent laboratory: Using llm agents as research assistants
Schmidgall, S., Su, Y., Wang, Z., Sun, X., Wu, J., Yu, X., Liu, J., Liu, Z., and Barsoum, E. (2025) · 2025
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Agentic retrieval-augmented generation: A survey on agentic rag
Singh, A., Ehtesham, A., Kumar, S., and Khoei, T. T. (2025) · 2025
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R1-searcher: Incentivizing the search capability in llms via reinforcement learning
Song, H., Jiang, J., Min, Y., Chen, J., Chen, Z., Zhao, W. X., Fang, L., and Wen, J.-R. (2025) · 2025
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Stop overthinking: A survey on efficient reasoning for large language models
Sui, Y., Chuang, Y.-N., Wang, G., Zhang, J., Zhang, T., Yuan, J., Liu, H., Wen, A., Zhong, S., Chen, H., et al. (2025) · 2025
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Agentic reasoning: Reasoning llms with tools for the deep research
Wu, J., Zhu, J., and Liu, Y. (2025) · 2025
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Improving retrieval-augmented generation in medicine with iterative follow-up questions
Xiong, G., Jin, Q., Wang, X., Zhang, M., Lu, Z., and Zhang, A. (2024) · 2025
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Cold-start recommendation with knowledge-guided retrieval-augmented generation
Yang, W., Zhang, W., Liu, Y., Han, Y., Wang, Y., Lee, J., and Yu, P. S. (2025) · 2025
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Inference scaling for long-context retrieval augmented generation
Yue, Z., Zhuang, H., Bai, A., Hui, K., Jagerman, R., Zeng, H., Qin, Z., Wang, D., Wang, X., and Bendersky, M. (2025) · 2025
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Dynamic text bundling supervision for zero-shot inference on text-attributed graphs
Zhao, Y., Zhang, Q., Luo, X., Zhang, W., Xiao, Z., Ju, W., Yu, P. S., and Zhang, M. (2025) · 2025
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Browsecomp-zh: Benchmarking web browsing ability of large language models in chinese
Zhou, P., Leon, B., Ying, X., Zhang, C., Shao, Y., Ye, Q., Chong, D., Jin, Z., Xie, C., Cao, M., et al. (2025) · 2025
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