Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
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Emily Dinan, Samuel Humeau, Bharath Chintagunta, Jason Weston. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Recent methodologies demonstrate a significant reduction in explicit bias and toxicity within model outputs, primarily by leveraging the models' ability to identify such undesirable behaviors upon inquiry.
“Recent methodologies demonstrate a significant reduction in explicit bias and toxicity within model outputs (Dinan et al. 2019; Bai et al. 2022; Ganguli et al. 2023), primarily by leveraging the models' ability to identify such undesirable behaviors upon inquiry.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Dinan et al. 2019
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Phadke, S., Samory, M., & Mitra, T. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479855 · Cited by 23 · see citing works
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering
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Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
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SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems
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Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
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Omar, M., & Mohaisen, A. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524667 · Cited by 17 · see citing works
On-Device Smishing Classifier Resistant to Text Evasion Attack
Seo, J. W., Lee, J., Kim, H., Lee, J., Han, S., Cho, J., & Lee, C.-H. (2024) · IEEE Access · doi:10.1109/access.2024.3349577 · Cited by 15 · see citing works
Tekiroğlu, S. S., Bonaldi, H., Fanton, M., & Guerini, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.245 · Cited by 18 · see citing works
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design
Bergman, A. S., Abercrombie, G., Spruit, S. L., Hovy, D., Dinan, E., Boureau, Y.-L., & Rieser, V. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.4 · Cited by 16 · see citing works
Self-citationConversational AI—A State‐of‐the‐Art Review
Bhardwaj, V., Kumar, M., Joshi, D., Chourasia, A., Bawaskar, B., & Sharma, S. (2024) · doi:10.1002/9781394200801.ch31 · Cited by 16 · see citing works
Nguyen, A., Rai, A., & Maruping, L. (2024) · Journal of Management Information Systems · doi:10.1080/07421222.2024.2340831 · Cited by 9 · see citing works
Ocampo, N. B., Cabrio, E., & Villata, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.173 · Cited by 15 · see citing works
Deploying Lifelong Open-Domain Dialogue LearningPreprint
Shuster, K., Urbanek, J., Dinan, E., Szlam, A. D., & Weston, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08076 · Cited by 15 · see citing works
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Li, C., Xing, W., Song, Y., & Lyu, B. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100338 · Cited by 20 · see citing works
Adversarial Examples for Evaluating Math Word Problem Solvers
Kumar, V., Maheshwary, R., & Pudi, V. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.230 · Cited by 14 · see citing works
Know your enemy: Conversational agents for security, education, training, and awareness at scale
Giboney, J. S., Schuetzler, R. M., & Grimes, G. M. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103207 · Cited by 13 · see citing works
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions
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AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow
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Analyzing Dynamic Adversarial Training Data in the Limit
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A Benchmark for Understanding Dialogue Safety in Mental Health Support
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Robust Conversational Agents against Imperceptible Toxicity Triggers
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Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems
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Legilimens: Practical and Unified Content Moderation for Large Language Model Services
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- Authors
- Dinan, E., Humeau, S., Chintagunta, B., & Weston, J.
- Affiliations
- Meta (Israel) (IL)
- Virginia Tech (US)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 4536-4545
- OpenAlex
- W2969117066
- Cited by
- 161
- References
- 15
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 17 · 2023: 34 · 2022: 32 · 2021: 36 · 2020: 19 · 2019: 2
- FWCI
- 8.51
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- adversarial attacks, robustness, chatbots, adversarial training, dialogue systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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