Elamur Research⌕
☰
home / papers / 2023 / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research

PaperQA: Retrieval-Augmented Generative Agent for Scientific Research

Paper · 2023 · Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski et al.

Summary

Scientific literature grows faster than researchers can read it, and plain LLMs answering science questions tend to invent facts, give no traceable sources, and know nothing published after their training cutoff. The authors present PaperQA, which turns retrieval-augmented generation into a flexible agent instead of a fixed pipeline. An orchestrating LLM controls three tools. The first searches online literature (e.g., Arxiv, Pubmed) and indexes full-text papers. The second retrieves and summarizes relevant passages, which an LLM then scores for relevance. The third composes a cited answer from that evidence in a map-reduce fashion. The agent can repeat searches with new keywords or rephrase its queries when the evidence is too thin. On a closed-book version of PubMedQA, it far outperforms GPT-4. The team also built LitQA, a harder benchmark of questions drawn from recent full-text papers outside LLM training data. On LitQA, PaperQA beats other models and commercial tools and performs on par with human experts at much lower cost. It also says "unsure" more often instead of answering wrongly, which matters for trustworthy literature-based research.

Key points

  • On PubMedQA with context removed, PaperQA scored 86.3% versus 57.9% for GPT-4. Its self-found evidence (86.3) slightly exceeded the provided ground-truth context (85.2), and combining both reached 90.1.
  • Architecture: three tools (search, gather evidence, answer question) driven by an agent LLM, with separate summary, ask, and answer LLM instances. Papers are split into overlapping 4,000-character chunks, embedded with text-embedding-ada-002, and retrieved via maximal marginal relevance.
  • The system adds LLM-generated relevance scores on top of vector similarity and uses a priori and a posteriori prompting to draw on the model's latent knowledge.
  • In paper-retrieval tests on LitQA, Google Scholar found far more source papers than Semantic Scholar (normalized AUC 0.66 vs 0.19 with GPT-4 keywords). The authors attribute this to Google Scholar's full-text search. GPT-4 slightly outperformed Claude-2 at generating keywords.
  • When background knowledge conflicts with the retrieved context, PaperQA declines to answer. When the retrieved context is insufficient, supportive background knowledge lets it respond.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of OxfordOxford · United Kingdom122 works · 160,277 citations🎓University of RochesterRochester · United States43 works · 7,874 citations🤝Future House, Inc.San Francisco · United States4 works · 548 citations🔬The Francis Crick InstituteLondon · United Kingdom2 works · 198 citations

Also on the byline: The Align Foundation.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksInformation Systems · 1,730 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 552 works

Cite

Cite as Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G. Rodriques and Andrew D. White. 2023. PaperQA: Retrieval-Augmented Generative Agent for Scientific Research. arXiv:2312.07559.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2312.07559 · arXiv 2312.07559

@misc{lala2023paperqa,
  title = {{PaperQA: Retrieval-Augmented Generative Agent for Scientific Research}},
  author = {Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G. Rodriques and Andrew D. White},
  year = {2023},
  doi = {10.48550/arxiv.2312.07559},
  eprint = {2312.07559},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.07559}
}

References

78 references, 40 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Dimitrije Curcic. Dimitrije curcic, Jun 2023. URL https://wordsrated.com/number-of-academic-papers-published-per-year/\#: ∼ :text=As%20of%202022%2C%20over%205.14,5. 03%20million%20papers%20were%20published. ↗
  2. Michael Fire and Carlos Guestrin. Over-optimization of academic publishing metrics: observing goodhart’s law in action. GigaScience, 8(6):giz053, 2019. find
  3. Santo Fortunato, Carl T Bergstrom, Katy Börner, James A Evans, Dirk Helbing, Staša Milojević, Alexander M Petersen, Filippo Radicchi, Roberta Sinatra, Brian Uzzi, et al. Science of science. Science, 359(6379):eaao0185, 2018. find
  4. Gunther S Stent. Prematurity and uniqueness in scientific discovery. Scientific American, 227 (6):84–93, 1972. find
  5. Eugene Garfield. Premature discovery or delayed recognition-why. Current contents, (21): 5–10, 1980. find
  6. Google Scholar. Google scholar. URL https://scholar.google.com/. ↗
  7. Rodney Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, et al. The semantic scholar open data platform. arXiv preprint arXiv:2301.10140, 2023. find
  8. Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S Weld. Specter: Document-level representation learning using citation-informed transformers. arXiv preprint arXiv:2004.07180, 2020.
  9. Iz Beltagy, Kyle Lo, and Arman Cohan. SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, Novemb…
  10. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information, 2017.
  11. Amanpreet Singh, Mike D’Arcy, Arman Cohan, Doug Downey, and Sergey Feldman. Scirepeval: A multi-format benchmark for scientific document representations. ArXiv, abs/2211.13308, 2022. find
  12. Paul Resnick and Hal R Varian. Recommender systems. Communications of the ACM, 40(3): 56–58, 1997. find
  13. Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6):734–749, 2005. find
  14. Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. Large language models encode clinical knowledge. Nature, pages 1–9, 2023.
  15. Nigel Williams, Stanislav Ivanov, and Dimitrios Buhalis. Algorithmic ghost in the research shell: Large language models and academic knowledge creation in management research, 2023. find
  16. W. Hiesinger, C. Zakka, Akash Chaurasia, R. Shad, Alex R. Dalal, Jennifer Kim, Michael Moor, Kevin Alexander, Euan A. Ashley, Jack Boyd, Kathleen Boyd, Karen Hirsch, C. Langlotz, and Joanna Nelson. Almanac: Retrieval-augmented language models for clinical medicine. Research Square, 2023. find
  17. Bertalan Meskó and Eric J Topol. The imperative for regulatory oversight of large language models (or generative ai) in healthcare. NPJ Digital Medicine, 6(1):120, 2023. find
  18. Phil Williamson. Take the time and effort to correct misinformation. Nature, 540(7632):171– 171, 2016. find
  19. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474, 2020.
  20. Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567, 2021.
  21. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. PubMedQA: A dataset for biomedical research question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing…
  22. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  23. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  24. Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.
  25. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
  26. Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378, 2023. find
  27. OpenAI. Gpt-4 technical report, 2023.
  28. Fan Bai, Alan Ritter, Peter Madrid, Dayne Freitag, and John Niekrasz. SynKB: Semantic search for synthetic procedures. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 311–318, Abu Dhabi, UAE, December 2022. Association for Compu… find
  29. Ronen Tamari, Fan Bai, Alan Ritter, and Gabriel Stanovsky. Process-level representation of scientific protocols with interactive annotation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2190–2202, Online, April 202… find
  30. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1–23, 2021.
  31. Patrick Lewis, Myle Ott, Jingfei Du, and Veselin Stoyanov. Pretrained language models for biomedical and clinical tasks: Understanding and extending the state-of-the-art. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, Online, November 2020. Association for Computational Lin…
  32. Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu. Biogpt: generative pre-trained transformer for biomedical text generation and mining. Briefings in Bioinformatics, 23(6), 2022. find
  33. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020.
  34. Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, and Raghav Mani. Biomegatron: Larger biomedical domain language model. arXiv preprint arXiv:2010.06060, 2020. find
  35. Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085, 2022.
  36. Mayk Caldas Ramos, Shane S Michtavy, Marc D Porosoff, and Andrew D White. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341, 2023. find
  37. Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. Minedojo: Building open-ended embodied agents with internet-scale knowledge, 2022.
  38. Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning, 2023.
  39. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560, 2022. find
  40. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
  41. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023. find
  42. Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, et al. Mrkl systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. arXiv preprin… — [arXiv:2205.00445](https://arxiv.org/abs/2205.00445) find
  43. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023.
  44. Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580, 2023.
  45. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022.
  46. Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. Mm-react: Prompting chatgpt for multimodal reasoning and action. arXiv preprint arXiv:2303.11381, 2023.
  47. Yashar Talebirad and Amirhossein Nadiri. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv preprint arXiv:2306.03314, 2023.
  48. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023. find
  49. Significant-gravitas. auto-gpt: An experimental open-source attempt to make gpt-4 fully autonomous., 2023. find
  50. Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension. In Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pages 4999–50… find
  51. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020.
  52. Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, and Jaewoo Kang. Can language models be biomedical knowledge bases? arXiv preprint arXiv:2109.07154, 2021.
  53. Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, and Xiangliang Zhang. What indeed can gpt models do in chemistry? a comprehensive benchmark on eight tasks, 2023. find
  54. Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande. Moleculenet: A benchmark for molecular machine learning, 2017. URL https://arxiv.org/abs/1703.00564. — [arXiv:1703.00564](https://arxiv.org/abs/1703.00564) ↗
  55. Andres M Bran, Sam Cox, Andrew D White, and Philippe Schwaller. Chemcrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376, 2023.
  56. Debadutta Dash, Rahul Thapa, Juan M Banda, Akshay Swaminathan, Morgan Cheatham, Mehr Kashyap, Nikesh Kotecha, Jonathan H Chen, Saurabh Gombar, Lance Downing, et al. Evaluation of gpt-3.5 and gpt-4 for supporting real-world information needs in healthcare delivery. arXiv preprint arXiv:2304.13714, 20… find
  57. D. Soong, S. Sridhar, Han Si, J. Wagner, Ana Caroline Costa S’a, Christina Y. Yu, Kubra Karagoz, Meijian Guan, Hisham K Hamadeh, and Brandon Higgs. Improving accuracy of gpt-3/4 results on biomedical data using a retrieval-augmented language model. ArXiv, abs/2305.17116, 2023. — [arXiv:2305.17116](https://arxiv.org/abs/2305.17116) find
  58. Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp. arXiv preprint arXiv:2212.14024, 2022.
  59. Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. Investigating the factual knowledge boundary of large language models with retrieval augmentation, 2023.
  60. Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. Active retrieval augmented generation. arXiv preprint arXiv:2305.06983, 2023.
  61. Gautier Izacard and Edouard Grave. Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282, 2020.
  62. Ehsan Doostmohammadi, Tobias Norlund, Marco Kuhlmann, and Richard Johansson. Surface-based retrieval reduces perplexity of retrieval-augmented language models. arXiv preprint arXiv:2305.16243, 2023. find
  63. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  64. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. Replug: Retrieval-augmented black-box language models. arXiv preprint arXiv:2301.12652, 2023.
  65. Kelvin Guu, Kenton Lee, Z. Tung, Panupong Pasupat, and Ming-Wei Chang. Realm: Retrieval-augmented language model pre-training. ArXiv, abs/2002.08909, 2020.
  66. Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147, 2022.
  67. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference on machine learning, p… find
  68. Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng Cao, Sebastian Schelter, and Ce Zhang. Improving retrieval-augmented large language models via data importance learning. arXiv preprint arXiv:2307.03027, 2023. find
  69. Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al. Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005, 2022.
  70. Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535–547, 2019.
  71. Jaime Carbonell and Jade Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SI-GIR conference on Research and development in information retrieval, pages 335–336, 1998. find
  72. Harrison Chase. Langchain. https://github.com/hwchase17/langchain, 2022. URL https://github.com/hwchase17/langchain. ↗
  73. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. arXiv preprint arXiv:2009.13081, 2020.
  74. George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, et al. An overview of the bioasq large-scale biomedical semantic indexing and question answering com… find
  75. Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, and Zhiting Hu. Rlprompt: Optimizing discrete text prompts with reinforcement learning, 2022.
  76. Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. arXiv preprint arXiv:2309.03409, 2023.
  77. Fabio Urbina, Filippa Lentzos, Cédric Invernizzi, and Sean Ekins. A teachable moment for dual-use. Nature machine intelligence, 4(7):607–607, 2022. find
  78. Petr Knoth, Drahomira Herrmannova, Matteo Cancellieri, Lucas Anastasiou, Nancy Pontika, Samuel Pearce, Bikash Gyawali, and David Pride. CORE: A global aggregation service for open access papers. Scientific Data, 10(1), June 2023. doi: 10.1038/s41597-023-02208-w. URL https://doi.org/10.1038/s41597-02… — [doi:10.1038/s41597-023-02208-w](https://doi.org/10.1038/s41597-023-02208-w) ↗

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/lala-2023-paperqa-retrieval-augmented-generative/