Bot-Adversarial Dialogue for Safe Conversational Agents

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Automated Hate Speech Detection and the Problem of Offensive Language

Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610 · see citing works

A Survey on Hate Speech Detection using Natural Language Processing

Schmidt, A. G., & Wiegand, M. (2017) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/w17-1101 · Cited by 1,447 · see citing works

A Deep Reinforced Model for Abstractive Summarization

Paulus, R., Xiong, C., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1705.04304 · Cited by 1,182 · see citing works

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.30 · Cited by 1,076 · see citing works

Reliability in content analysis: some common misconceptions and recommendations.

Krippendorff, K. (2004) · Human Communication Research · doi:10.1093/hcr/30.3.411 · Cited by 1,027 · see citing works

The Pushshift Reddit Dataset

Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7347 · Cited by 881 · see citing works

Real conversations with artificial intelligence: a comparison between human–human online conversations and human–chatbot conversations

Hill, J., Randolph Ford, W., & Farreras, I. G. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.02.026 · Cited by 856 · see citing works

The Risk of Racial Bias in Hate Speech Detection

Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817 · see citing works

Recipes for Building an Open-Domain Chatbot

Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y.-L., et al. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.24 · Cited by 784 · see citing works

Predicting the Type and Target of Offensive Posts in Social Media

Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1144 · Cited by 781 · see citing works

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.301 · Cited by 693 · see citing works

Measuring and Mitigating Unintended Bias in Text Classification

Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674 · see citing works

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.441 · Cited by 656 · see citing works

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Chen, Y., Zhou, Y., Zhu, S., & Xu, H. (2012) · International Conference on Social Computing · doi:10.1109/socialcom-passat.2012.55 · Cited by 653 · see citing works

Ex Machina: Personal Attacks Seen at Scale

Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636 · see citing works

Towards a Human-like Open-Domain Chatbot

Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614 · see citing works

Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint

Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., & Weston, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01241 · Cited by 610 · see citing works

ParlAI: A Dialog Research Software Platform

Miller, A., Feng, W., Batra, D., Bordes, A., Fisch, A., Lu, J., Parikh, D., & Weston, J. (2017) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d17-2014 · Cited by 326 · see citing works

Neural Text Generation with Unlikelihood Training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2019) · arXiv · doi:10.48550/arxiv.1908.04319 · Cited by 254 · see citing works

Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack

Dinan, E., Humeau, S., Chintagunta, B., & Weston, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1461 · Cited by 161 · see citing works

The Pushshift Reddit Dataset

Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3608134 · Cited by 145 · see citing works

I hate you! Disinhibition with virtual partners

Angeli, A. D., & Brahnam, S. (2008) · Interacting with Computers · doi:10.1016/j.intcom.2008.02.004 · Cited by 140 · see citing works

ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn ComparisonsPreprint

Li, M., Weston, J., & Roller, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03087 · Cited by 111 · see citing works

Neural Text Generation With Unlikelihood Training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 106 · see citing works

Why We Should Have Seen That Coming

Wolf, M. J., Miller, K., & Grodzinsky, F. S. (2017) · ORBIT Journal · doi:10.29297/orbit.v1i2.49 · Cited by 96 · see citing works

Empathy Is All You Need: How a Conversational Agent Should Respond to Verbal Abuse

Chin, H., Molefi, L. W., & Yi, M. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376461 · Cited by 96 · see citing works

Automatic detection of hate speech in text: an overview of the topic and dataset annotation with hierarchical classes

Fortuna, P. (2017) · Open Repository of the University of Porto (University of Porto) · doi:10.34626/hy8t-f260 · Cited by 47 · see citing works

Judgment of the Humanness of an Interlocutor Is in the Eye of the Beholder

Lortie, C. L., & Guitton, M. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025085 · Cited by 46 · see citing works

Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative ConversationsPreprint

Paranjape, A., See, A., Kenealy, K., Li, H., Hardy, A., Qi, P., Sadagopan, K. R., Phu, N. M., Soylu, D., & Manning, C. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12348 · Cited by 32 · see citing works

Should an Agent Be Ignoring It?

Chin, H., & Yi, M. Y. (2019) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3312826 · Cited by 28 · see citing works

Stupid computer! Abuse and social identities

Angeli, A. D. (2005) · Institutional Research Information System (Università degli Studi di Trento) · doi:10.1007/11555261 · Cited by 26 · see citing works

A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents

Curry, A. C., & Rieser, V. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5942 · Cited by 22 · see citing works

Red Teaming Language Models with Language Models

Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.225 · Cited by 363 · see citing works

Predictability and Surprise in Large Generative Models

Ganguli, D., Hernandez, D., Lovitt, L., Askell, A., Bai, Y., Chen, A., Conerly, T., Dassarma, N., Drain, D., Elhage, N., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533229 · Cited by 207 · see citing works

Trustworthy ai: A computational perspective

Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261 · see citing works

COLD: A Benchmark for Chinese Offensive Language Detection

Deng, J., Zhou, J., Sun, H., Zheng, C., Mi, F., Meng, H. M. L., & Huang, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.796 · Cited by 103 · see citing works

“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset

Smith, E. M., Hall, M., Kambadur, M., Presani, E., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.625 · Cited by 97 · see citing works

ProsocialDialog: A prosocial backbone for conversational agents

Kim, H., Yu, Y., Jiang, L., Lu, X., Khashabi, D., Kim, G., Choi, Y., & Sap, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.267 · Cited by 80 · see citing works

The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems

Ziems, C., Yu, J., Wang, Y., Halevy, A. Y., & Yang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.261 · Cited by 73 · see citing works

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.09251 · Cited by 53 · see citing works

On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark

Sun, H., Xu, G., Deng, J., Cheng, J., Zheng, C., Zhou, H., Peng, N., Zhu, X., & Huang, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.308 · Cited by 49 · see citing works

A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects

Deng, Y., Lei, W., Lam, W. P., & Chua, T. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/738 · Cited by 47 · see citing works

Data Governance in the Age of Large-Scale Data-Driven Language Technology

Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534637 · Cited by 45 · see citing works

Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities

Deng, Y., Liao, L., Lei, W., Yang, G. H., Lam, W. P., & Chua, T. (2025) · ACM Transactions on Information Systems · doi:10.1145/3715097 · Cited by 43 · see citing works

Why So Toxic?

Si, W. M., Michael Backes, A., Blackburn, J., Cristofaro, E. D., Stringhini, G., Zannettou, S., & Zhang, Y. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560599 · Cited by 47 · see citing works

InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

Gupta, P., Jiao, C., Yeh, Y.-T., Mehri, S., Eskénazi, M., & Bigham, J. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.33 · Cited by 46 · see citing works

SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems

Dinan, E., Abercrombie, G., Bergman, A., Spruit, S., Hovy, D., Boureau, Y.-L., & Rieser, V. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.284 · Cited by 32 · see citing works

Keep Me Updated! Memory Management in Long-term Conversations

Bae, S., Kwak, D., Kang, S., Lee, M. Y., Kim, S., Jeong, Y., Kim, H., Lee, S.-W., Park, W., & Sung, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.276 · Cited by 29 · see citing works

Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

Bae, S., Kwak, D., Kim, S., Ham, D., Kang, S., Lee, S., & Park, W. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.155 · Cited by 28 · see citing works

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluation Methods

Zhou, Y., Ni, T., Lee, W., & Zhao, Q. (2025) · Transactions on Artificial Intelligence · doi:10.53941/tai.2025.100003 · Cited by 34 · see citing works

SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures

Ung, M., Xu, J., & Boureau, Y.-L. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.447 · Cited by 24 · see citing works

A prompt-based approach to adversarial example generation and robustness enhancement

Yang, Y., Huang, P., Cao, J., Li, J., Lin, Y., & Ma, F. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-2639-2 · Cited by 32 · see citing works

Comprehensive framework for implementing blockchain-enabled federated learning and full homomorphic encryption for chatbot security system

Jalali, N. A., & Hongsong, C. (2024) · Cluster Computing · doi:10.1007/s10586-024-04515-2 · Cited by 21 · see citing works

AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Radharapu, B., Robinson, K., Aroyo, L. M., & Lahoti, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.37 · Cited by 34 · see citing works

Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots

Chen, B., Wang, G., Guo, H., Wang, Y., & Yan, Q. (2023) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3607199.3607237 · Cited by 22 · 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

KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications

Lee, H., Hong, S., Park, J., Kim, T., Kim, G., & Ha, J.-W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track) · doi:10.18653/v1/2023.acl-industry.21 · Cited by 15 · see citing works

The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

Kirk, H. R., Bean, A., Vidgen, B., Röttger, P., & Hale, S. A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.148 · Cited by 26 · see citing works

Using In-Context Learning to Improve Dialogue Safety

Meade, N., Gella, S., Hazarika, D., Gupta, P., Jin, D., Reddy, S., Liu, Y., & Hakkani-Tur, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.796 · Cited by 13 · see citing works

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow

Deng, D., Zhang, C., Zheng, H., Pu, Y., Ji, S., & Wu, Y. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456150 · Cited by 13 · see citing works

Collecting Qualitative Data at Scale with Large Language Models: A Case Study

Cuevas, A., Scurrell, J. V., Brown, E. M., Entenmann, J., & Daepp, M. I. G. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710947 · Cited by 23 · see citing works

Analyzing Dynamic Adversarial Training Data in the Limit

Wallace, E., Williams, A., Jia, R., & Kiela, D. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.18 · Cited by 15 · see citing works

Deceiving LLM through Compositional Instruction with Hidden Attacks

Jiang, S., Chen, X., & Tang, R. (2025) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3717067 · Cited by 10 · see citing works

AURA: Amplifying Understanding, Resilience, and Awareness for Responsible AI Content Work

Zhang, A. Q., Amores, J., Shen, H., Czerwinski, M. P., Gray, M. L., & Suh, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710931 · Cited by 13 · see citing works

Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems

Finch, S. E., Finch, J. D., & Choi, J. D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.839 · Cited by 10 · see citing works

A First Look at Toxicity Injection Attacks on Open-domain Chatbots

Weeks, C., Cheruvu, A., Abdullah, S. M., Kanchi, S., Yao, D. D., & Viswanath, B. (2023) · Annual Computer Security Applications Conference · doi:10.1145/3627106.3627122 · Cited by 10 · see citing works

Revisiting Contextual Toxicity Detection in Conversations

Anuchitanukul, A., Ive, J., & Specia, L. (2022) · Journal of Data and Information Quality · doi:10.1145/3561390 · Cited by 10 · see citing works

ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models

Mei, A., Levy, S., & Wang, W. Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.388 · Cited by 9 · see citing works

The CRINGE Loss: Learning what language not to model

Adolphs, L., Gao, T., Xu, J., Shuster, K., Sukhbaatar, S., & Weston, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.493 · Cited by 10 · see citing works

The Bias that Lies Beneath: Qualitative Uncovering of Stereotypes in Large Language Models

Babonnaud, W., Delouche, E., & Lahlouh, M. (2024) · Linköping electronic conference proceedings · doi:10.3384/ecp208022 · Cited by 6 · see citing works

On safe and usable chatbots for promoting voter participation

Muppasani, B. C., Pallagani, V., Lakkaraju, K., Lei, S., Srivastava, B., Robertson, B. W., Hickerson, A., & Narayanan, V. (2023) · AI Magazine · doi:10.1002/aaai.12109 · Cited by 6 · see citing works

Those Aren’t Your Memories, They’re Somebody Else’s: Seeding Misinformation in Chat Bot Memories

Atkins, C., Zhao, B. Z. H., Asghar, H. J., Wood, I., & Kâafar, M. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33488-7_11 · Cited by 5 · see citing works

SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration

Lee, H., Hong, S., Park, J., Kim, T., Cha, M., Choi, Y., Kim, B., Kim, G., Lee, E., Lim, Y., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.370 · Cited by 5 · see citing works

Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning

Zheng, C., Ke, P., Zhang, Z., & Huang, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.65 · Cited by 5 · see citing works

Query-Efficient Black-Box Red Teaming via Bayesian Optimization

Lee, D., Lee, J., Ha, J.-W., Kim, J.-H., Lee, S.-W., Lee, H., & Song, H. O. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.646 · Cited by 5 · see citing works

Jailbreaking large language models: models, origins, and evolution of attacks and defenses

Li, X. H., Wu, J., Zheng, Q., Wang, H., Fan, M. Y., Hu, S., Guo, J., & Ting, L. I. U. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2024-0196 · Cited by 4 · see citing works

The State of Profanity Obfuscation in Natural Language Processing Scientific Publications

Nozza, D., & Hovy, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.240 · Cited by 4 · see citing works

Improving Bot Response Contradiction Detection via Utterance Rewriting

Jin, D., Liu, S., Liu, Y., & Hakkani-Tür, D. Z. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.56 · Cited by 4 · see citing works

Evolving Diverse Red-Team Language Models in Multi-Round Multi-Agent Games

Ma, C.-D., Yang, Z., Ci, H., Gao, J., Gao, M.-Q., Pan, X.-H., & Yang, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3694367 · Cited by 20 · see citing works

Organization Matters: A Qualitative Study of Organizational Dynamics in Red Teaming Practices For Generative AI

Ren, B., Cheon, E., & Li, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757641 · Cited by 6 · see citing works

Towards Agile Text Classifiers for Everyone

Mozes, M., Hoffmann, J., Tomanek, K., Kouate, M., Thain, N., Yuan, A., Bolukbasi, T., & Dixon, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.30 · Cited by 5 · see citing works

IberBench: LLM evaluation on Iberian languages

González, J. Á., Borrego-Obrador, I., Herrero, Á. R., Sarvazyan, A. M., Chinea-Ríos, M., Basile, A., & Franco-Salvador, M. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101899 · Cited by 3 · see citing works

Sources of Noise in Dialogue and How to Deal with Them

Chen, D., & Zhou, Y. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.1 · Cited by 2 · see citing works

Adversarial Attacks on IoT Systems Leveraging Large Language Models

Shan, W., Long, T., & Zhou, Z. (2024) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki65561.2024.00035 · Cited by 1 · see citing works

Can Personal Health Information Be Secured in LLM? Privacy Attack and Defense in the Medical Domain

Kang, Y., Kim, E., & Cho, Y.-S. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3765105 · Cited by 1 · see citing works

From Human Pragmatic Language Skills to Conversational Agent Design: A Systematic Review of Transfer Strategies

Hu, J., Yao, X., Huang, Z., Liang, D., Yang, D., Liu, D., Li, J., Zhang, Y., & Ma, X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3792787 · Cited by 1 · see citing works

Cheap Learning: Maximizing Performance of Language Models for Social Data Science Using Minimal Data

Castro-González, L., Chung, Y.-L., Kirk, H. R., Francis, J., Williams, A. R., Johansson, P., & Bright, J. (2025) · Sociological Methods & Research · doi:10.1177/00491241251340608 · Cited by 1 · see citing works

DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines

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Auditable LLM Safety-Judge Selection via Confidence-Interval Filtering on Unsafe Recall

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Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack

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Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems

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Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey

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SafeConv: Explaining and Correcting Conversational Unsafe Behavior

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