Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models

Author:

Parry AndrewORCID,Fröbe MaikORCID,MacAvaney SeanORCID,Potthast MartinORCID,Hagen MatthiasORCID

Publisher

Springer Nature Switzerland

Reference47 articles.

1. Akkalyoncu Yilmaz, Z., Yang, W., Zhang, H., Lin, J.: Cross-domain modeling of sentence-level evidence for document retrieval. 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), pp. 3490–3496, Association for Computational Linguistics, Hong Kong, China (2019). https://aclanthology.org/D19-1352

2. Askari, A., Aliannejadi, M., Kanoulas, E., Verberne, S.: A test collection of synthetic documents for training rankers: Chatgpt vs. human experts. In: Frommholz, I., Hopfgartner, F., Lee, M., Oakes, M., Lalmas, M., Zhang, M., Santos, R.L.T. (eds.) Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21–25, 2023, pp. 5311–5315. ACM (2023)

3. Bajaj, P., et al.: MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. CEUR Workshop Proceedings 1773 (2016). ISSN 16130073, https://arxiv.org/abs/1611.09268v3, publisher: CEUR-WS

4. Brown, T.B., et al.: Language models are few-shot learners. arXiv:2005.14165 (2020)

5. Camara, A., Hauff, C.: Diagnosing BERT with retrieval heuristics. In: Jose, J.M., Yilmaz, E., Magalhaes, J., Castells, P., Ferro, N., Silva, M.J., Martins, F. (eds.) Advances in Information Retrieval, pp. 605–618, Lecture Notes in Computer Science, Springer International Publishing, Cham (2020). ISBN 978-3-030-45439-5, https://doi.org/10.1007/978-3-030-45439-5_40

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