Poisoning medical knowledge using large language models

Roberts, R. J. PubMed Central: the GenBank of the published literature. Proc. Natl Acad. Sci. USA 98, 381–382 (2001).Article 

Google Scholar 
Canese, K. & Weis, S. in The NCBI Handbook 2nd edn (eds Beck, J. et al.) Ch. 3 (NCBI, 2013).Percha, B. & Altman, R. B. A global network of biomedical relationships derived from text. Bioinformatics 34, 2614–2624 (2018).Article 

Google Scholar 
Rossanez, A., Dos Reis, J. C., Torres, R., da, S. & de Ribaupierre, H. KGen: a knowledge graph generator from biomedical scientific literature. BMC Med. Inform. Decis. Mak. 20, 314 (2020).Article 

Google Scholar 
Asada, M., Miwa, M. & Sasaki, Y. Using drug descriptions and molecular structures for drug–drug interaction extraction from literature. Bioinformatics 37, 1739–1746 (2021).Turki, H., Hadj Taieb, M. A. & Ben Aouicha, M. MeSH qualifiers, publication types and relation occurrence frequency are also useful for a better sentence-level extraction of biomedical relations. J. Biomed. Inform. 83, 217–218 (2018).Article 

Google Scholar 
Zeng, X., Tu, X., Liu, Y., Fu, X. & Su, Y. Toward better drug discovery with knowledge graph. Curr. Opin. Struct. Biol. 72, 114–126 (2022).Article 

Google Scholar 
Mohamed, S. K., Nounu, A. & Nováček, V. Biological applications of knowledge graph embedding models. Brief. Bioinform. 22, 1679–1693 (2021).Article 

Google Scholar 
MacLean, F. Knowledge graphs and their applications in drug discovery. Expert Opin. Drug Discov. 16, 1057–1069 (2021).Article 

Google Scholar 
Wang, S., Lin, M., Ghosal, T., Ding, Y. & Peng, Y. Knowledge graph applications in medical imaging analysis: a scoping review. Health Data Sci. 2022, 9841548 (2022).Article 

Google Scholar 
Ouyang, L. et al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems (eds Koyejo, S. et al.) 27730–27744 (2022).Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).
Google Scholar 
Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 5485–5551 (2020).MathSciNet 

Google Scholar 
Lewis, M. et al. BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proc. 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 7871–7880 (ACL, 2020).OpenAI et al. GPT-4 technical report. Preprint at https://arxiv.org/abs/2303.08774 (2023).Thoppilan, R. et al. LaMDA: language models for dialog applications. Preprint at https://arxiv.org/abs/2201.08239 (2022).Surameery, N. M. S. & Shakor, M. Y. Use Chat GPT to solve programming bugs. Int. J. Inform. Technol. Comput. Eng. 3, 17–22 (2023).
Google Scholar 
Biswas, S. S. Potential use of Chat GPT in global warming. Ann. Biomed. Eng. 51, 1126–1127 (2023).Article 

Google Scholar 
Biswas, S. S. Role of Chat GPT in public health. Ann. Biomed. Eng. 51, 868–869 (2023).Article 

Google Scholar 
Sallam, M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel) 11, 887 (2023).Article 

Google Scholar 
Park, J. S. et al. Generative agents: interactive simulacra of human behavior. In Proc. 36th Annual ACM Symposium on User Interface Software and Technology (eds Follmer, S. et al.) 2:1–2:22 (ACM, 2023).Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual-language foundation model for pathology image analysis using medical Twitter. Nat. Med. 29, 2307–2316 (2023).Article 

Google Scholar 
Sheldon, T. Preprints could promote confusion and distortion. Nature 559, 445 (2018).Article 

Google Scholar 
Methods, preprints and papers. Nat. Biotechnol. 35, 1113 (2017).Preprints in biology. Nat. Methods 13, 277 (2016).Watson, C. Rise of the preprint: how rapid data sharing during COVID-19 has changed science forever. Nat. Med. 28, 2–5 (2022).Article 

Google Scholar 
Wang, L. L. et al. CORD-19: the COVID-19 open research dataset. Preprint at https://www.arxiv.org/abs/2004.10706v4 (2020).Ahamed, S. & Samad, M. Information mining for COVID-19 research from a large volume of scientific literature. Preprint at https://arxiv.org/abs/2004.02085 (2020).Pestryakova, S. et al. CovidPubGraph: a FAIR knowledge graph of COVID-19 publications. Sci. Data 9, 1–11 (2022).Article 

Google Scholar 
Zhang, R. et al. Drug repurposing for COVID-19 via knowledge graph completion. J. Biomed. Inform. 115, 103696 (2021).Article 

Google Scholar 
Michel, F. et al. Covid-on-the-Web: knowledge graph and services to advance COVID-19 research. In Proc. 19th International Semantic Web Conference (eds Pan, J. Z. et al.) 294–310 (Springer, 2020).Gehrmann, S., Strobelt, H. & Rush, A. M. GLTR: statistical detection and visualization of generated text. In Proc. 57th Conference of the Association for Computational Linguistics (eds Korhonen, A. et al.) 111–116 (ACL, 2019).Jawahar, G., Abdul-Mageed, M. & Lakshmanan, L. V. S. Automatic detection of machine generated text: a critical survey. In Proc. 28th International Conference on Computational Linguistics (eds Scott, D. et al.) 2296–2309 (ICCL, 2020).Wang, W. & Feng, A. Self-information loss compensation learning for machine-generated text detection. Math. Probl. Eng. 2021, 6669468 (2021).
Google Scholar 
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D. & Finn, C. DetectGPT: zero-shot machine-generated text detection using probability curvature. In Proc. International Conference on Machine Learning (eds Krause, A. et al.) 24950–24962 (PMLR, 2023).Meyer zu Eissen, S. & Stein, B. Intrinsic plagiarism detection. In Proc. Advances in Information Retrieval (eds Lalmas, M. et al.) 565–569 (Springer Berlin Heidelberg, 2006).Lukashenko, R., Graudina, V. & Grundspenkis, J. Computer-based plagiarism detection methods and tools: an overview. In Proc. International Conference on Computer Systems and Technologies https://doi.org/10.1145/1330598.13306 (ACM, 2007).Meyer zu Eissen, S., Stein, B. & Kulig, M. Plagiarism detection without reference collections. In Advances in Data Analysis (eds Decker, R. & Lenz, H. J.) 359–366 (Springer Berlin Heidelberg, 2007).Donaldson, J. L., Lancaster, A.-M. & Sposato, P. H. A plagiarism detection system. In Proc. 12th SIGCSE Technical Symposium on Computer Science Education https://doi.org/10.1145/953049.800955 (ACM, 1981).Yang, B., Yih, W.-T., He, X., Gao, J. & Deng, L. Embedding entities and relations for learning and inference in knowledge bases. In 3rd International Conference on Learning Representations (ICLR, 2015).Dettmers, T., Minervini, P., Stenetorp, P. & Riedel, S. Convolutional 2D knowledge graph embeddings. In Proc. AAAI Conference on Artificial Intelligence 1811–1818 (AAAI, 2018).Trouillon, T., Welbl, J., Riedel, S., Gaussier, E. & Bouchard, G. Complex embeddings for simple link prediction. In Proc. 33rd International Conference on Machine Learning (eds. Balcan, M. F. & Weinberger, K. Q.) 2071–2080 (PMLR, 2016).Lu, Y. et al. Unified structure generation for universal information extraction. In Proc. 60th Annual Meeting of the Association for Computational Linguistics (eds Muresan, S. et al.) 5755–5772 (ACL, 2022).Li, X. et al. TDEER: an efficient translating decoding schema for joint extraction of entities and relations. In Proc. Conference on Empirical Methods in Natural Language Processing (eds Moens, M.-F. et al.) 8055–8064 (ACL, 2021).Yamada, I., Asai, A., Shindo, H., Takeda, H. & Matsumoto, Y. LUKE: deep contextualized entity representations with entity-aware self-attention. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing (eds Webber, B. et al.) 6442–6454 (ACL, 2020).Bhardwaj, P., Kelleher, J., Costabello, L. & O’Sullivan, D. Poisoning knowledge graph embeddings via relation inference patterns. In Proc. 59th Annual Meeting of the Association for Computational Linguistics (eds Zong, C. et al.) 1875–1888 (ACL 2021).Pezeshkpour, P., Tian, Y. & Singh, S. Investigating robustness and interpretability of link prediction via adversarial modifications. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics (eds Burstein, J. et al.) 3336–3347 (ACL, 2019).Bhardwaj, P., Kelleher, J., Costabello, L. & O’Sullivan, D. Adversarial attacks on knowledge graph embeddings via instance attribution methods. In Proc. 2021 Conference on Empirical Methods in Natural Language Processing (eds Moens, M.-F. et al.) 8225–8239 (ACL, 2021).Li, Q., Wang, Z. & Li, Z. PAGCL: an unsupervised graph poisoned attack for graph contrastive learning model. Future Gener. Comput. Syst. 149, 240–249 (2023).Article 

Google Scholar 
You, X. et al. MaSS: model-agnostic, semantic and stealthy data poisoning attack on knowledge graph embedding. In Proc. ACM Web Conference (eds Ding, Y et al.) 2000–2010 (ACM, 2023).Betz, P., Meilicke, C. & Stuckenschmidt, H. Adversarial explanations for knowledge graph embeddings. In Proc. 31st International Joint Conference on Artificial Intelligence (ed. De Raedt, L.) 2820–2826 (IJCAIO, 2022).Zhang, H. et al. Data poisoning attack against knowledge graph embedding. In Proc. 28th International Joint Conference on Artificial Intelligence (eds Kraus, S.) 4853–4859 (IJCAI, 2019).Gao, Z., Ding, P. & Xu, R. K. G.- Predict: a knowledge graph computational framework for drug repurposing. J. Biomed. Inform. 132, 104133 (2022).Article 

Google Scholar 
Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N. & Talukdar, P. InteractE: improving convolution-based knowledge graph embeddings by increasing feature interactions. In Proc. AAAI Conference on Artificial Intelligence 3009–3016 (AAAI, 2020).Han, S. et al. Standigm ASKTM: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Brief. Bioinform. 25, bbae035 (2024).Article 

Google Scholar 
Zheng, S. et al. PharmKG: a dedicated knowledge graph benchmark for bomedical data mining. Brief. Bioinform. 22, bbaa344 (2021).Article 

Google Scholar 
Wei, C.-H., Kao, H.-Y. & Lu, Z. PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res. 41, W518–W522 (2013).Article 

Google Scholar 
Greenhalgh, T. How to read a paper. The Medline database. Brit. Med. J. 315, 180–183 (1997).Article 

Google Scholar 
de Marneffe, M.-C. & Manning, C. D. The Stanford typed dependencies representation. In Proc. Workshop on Cross-Framework and Cross-Domain Parser Evaluation (eds Bos, J. et al.) 1–8 (ACL, 2008); https://doi.org/10.3115/1608858.1608859Page, L., Brin, S., Motwani, R. & Winograd, T. The PageRank citation ranking: bringing order to the web. http://ilpubs.stanford.edu:8090/422/ (Stanford InfoLab, 1999).Bodenreider, O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267–D270 (2004).Article 

Google Scholar 
Koh, P. W. & Liang, P. Understanding black-box predictions via influence functions. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 1885–1894 (PMLR, 2017).Bianchini, M., Gori, M. & Scarselli, F. Inside PageRank. ACM Trans. Internet Technol. 5, 92–128 (2005).Article 

Google Scholar 
Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform. 23, bbac409 (2022).Article 

Google Scholar 
Yuan, H. et al. BioBART: Pretraining and evaluation of a biomedical generative language model. In Proc. 21st Workshop on Biomedical Language Processing (eds Demner-Fushman, D. et al.) 97–109 (ACL, 2022).Liu, Y. et al. G-Eval: NLG Evaluation using GPT-4 with better human alignment. In Proc. 2023 Conference on Empirical Methods in Natural Language Processing (eds Bouamor, H. et al.) 2511–2522 (ACL, 2023).Chen, J., Lin, H., Han, X. & Sun, L. Benchmarking large language models in retrieval-augmented generation. In Proc. AAAI Conference on Artificial Intelligence (eds Woodridge, M. et al.) 17754–17762 (2024).Ranjit, M., et al. Retrieval augmented chest X-ray report generation using OpenAI GPT models. In Proc. 8th Machine Learning for Healthcare Conference (eds Deshpande, K. et al.) 650–666 (PMLR, 2023).Chandak, P., Huang, K. & Zitnik, M. Building a knowledge graph to enable precision medicine. Sci. Data 10, 67 (2023).Article 

Google Scholar 
Percha, B. & Altman, R. A global network of biomedical relationships derived from text. Zenodo zenodo.org/records/1035500 (2017).Junwei, Y. et al. Poisoning medical knowledge using large language models v.1.0.1. Zenodo https://doi.org/10.5281/zenodo.13191322 (2024).Lee, J. et al. BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234–1240 (2020).Article 

Google Scholar 

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