Attacking Open-domain Question Answering by Injecting Misinformation
Open accessAbstract
Liangming Pan, Wenhu Chen, Min-Yen Kan, William Yang Wang. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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If adherence is based on factual consistency, language models can be equipped with self-discrimination to evaluate and reconcile parametric and context information.
“Conversely, if adherence is based on factual consistency, language models can be equipped with self-discrimination to evaluate and reconcile parametric and context information [155, 156].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [155]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575 · see citing works
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844 · see citing works
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00300 · Cited by 1,827 · see citing works
Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546 · see citing works
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works
Latent retrieval for weakly supervised open domain question answering
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Learning to Ask: Neural Question Generation for Reading Comprehension
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“A possible reason is that most questions in SQuAD are shallow in reasoning (Du et al., 2017).”
BackgroundColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
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Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.565 · Cited by 290 · see citing works
All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · arXiv · doi:10.48550/arxiv.2107.00061 · Cited by 290
Did the Model Understand the Question?
Mudrakarta, P. K., Taly, A., Sundararajan, M., & Dhamdhere, K. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1176 · Cited by 166 · see citing works
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
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Flagged first, then by citations
Cited by
Sorted by citations
Human-inspired Perspectives: A Survey on AI Long-term Memory
He, Z., Lin, W., Zheng, H., Zhang, F., Jones, M. W., Aitchison, L., Xu, X., Liu, M., Kristensson, P. O., & Shen, J. (2024) · arXiv · doi:10.48550/arxiv.2411.00489 · Cited by 3 · see citing works
“Conversely, if adherence is based on factual consistency, language models can be equipped with self-discrimination to evaluate and reconcile parametric and context information [149, 150].”
SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models
Pham, T., Nguyen, N., Zunjare, P., Chen, W., Tseng, Y.-M., & Vu, T. (2025) · arXiv · doi:10.48550/arxiv.2506.01062
“Reasoning under knowledge conflict: Prior work shows that LLM S can be vulnerable to misinfor-mation [Pan et al., 2023], irrelevant context [Shi et al., 2023], and conflicting sources [Kazemi et al., 2023].”
Self-citation
- Authors
- Pan, L., Chen, W., Kan, M.-Y., & Wang, W. Y.
- Affiliations
- University of California, Santa Barbara (US)
- University of Waterloo (CA)
- National University of Singapore (SG)
- Venue
- Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 525-539
- OpenAlex
- W4392669708
- Cited by
- 9
- References
- 17
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3
- FWCI
- 1.59
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) (publishedVersion, cc-by)
- Funders
- National Science Foundation (2048122)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- open-domain question answering, misinformation, question answering evaluation
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