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Predicting Depression via Social Media

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A Survey on Hate Speech Detection using Natural Language Processing

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Personalizing Dialogue Agents: I have a dog, do you have pets too?

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The Pushshift Reddit Dataset

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Computing Krippendorff's Alpha-Reliability

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Recipes for Building an Open-Domain Chatbot

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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

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Ex Machina: Personal Attacks Seen at Scale

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Plug and Play Language Models: A Simple Approach to Controlled Text Generation

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Ethical and social risks of harm from Language Models

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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

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Physicians’ perceptions of chatbots in health Care: cross-sectional web-based survey

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Depression and Self-Harm Risk Assessment in Online Forums

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Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

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Conversational AI: The Science Behind the Alexa PrizePreprint

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HateCheck: Functional Tests for Hate Speech Detection Models

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MedDialog: Large-scale Medical Dialogue Datasets

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

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Recipes for Safety in Open-domain ChatbotsPreprint

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Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions

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AI and the Everything in the Whole Wide World Benchmark

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A Just and Comprehensive Strategy for Using NLP to Address Online Abuse

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

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Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets

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Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets

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Conversational AI: Social and Ethical Considerations.

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We Need to Consider Disagreement in Evaluation

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RankME: Reliable Human Ratings for Natural Language Generation

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Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants

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Fortifying Toxic Speech Detectors Against Veiled Toxicity

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Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models

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Advancing the State of the Art in Open Domain Dialog Systems through the Alexa PrizePreprint

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BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR

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XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages

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

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Ask Diana: A Keyword-Based Chatbot System for Water-Related Disaster Management

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A Distributional Approach to Controlled Text Generation

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OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results

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Naturally occurring language as a source of evidence in suicide prevention

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

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Alana: Social Dialogue using an Ensemble Model and a Ranker trained on User Feedback

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What’s to bullying a bot?

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Detect All Abuse! Toward Universal Abusive Language Detection Models

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Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies

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Four-Stage Framework for Implementing a Chatbot System in Disaster Emergency Operation Data Management: A Flood Disaster Management Case Study

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Detecting ‘Dirt’ and ‘Toxicity’: Rethinking Content Moderation as Pollution BehaviourPreprint

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Revealing Persona Biases in Dialogue SystemsPreprint

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Mitigating harm in language models with conditional-likelihood filtrationPreprint

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The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

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Towards Ethics by Design in Online Abusive Content DetectionPreprint

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Chat as Expected: Learning to Manipulate Black-box Neural Dialogue ModelsPreprint

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The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity

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Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and MethodologyPreprint

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Tartan: A retrieval-based socialbot powered by a dynamic finite-state machine architecturePreprint

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Large-Scale Quantitative Evaluation of Dialogue Agents’ Response Strategies against Offensive Users

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A Framework and Tool for Collaborative Extraction of Reliable Information

Neubig, G., Mori, S., & Mizukami, M. (2013) · Cited by 4 · see citing works

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Schick, T., Udupa, S., & Schütze, H. (2021) · arXiv · doi:10.48550/arxiv.2103.00453 · Cited by 1 · see citing works

"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust

Kim, S. S. Y., Liao, Q. V., Vorvoreanu, M., Ballard, S., & Vaughan, J. W. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658941 · Cited by 147 · see citing works

Mirages. On Anthropomorphism in Dialogue Systems

Abercrombie, G., Curry, A., Dinkar, T., Rieser, V., & Talat, Z. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.290 · Cited by 83 · see citing works

Proactive Conversational Agents in the Post-ChatGPT World

Liao, L., Yang, G. H., & Shah, C. S. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3594250 · Cited by 83 · see citing works

Assessment of a large language model based digital intelligent assistant in assembly manufacturing

Colabianchi, S., Costantino, F., & Sabetta, N. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104129 · Cited by 66 · 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

A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement

Sarkar, S., Gaur, M., Chen, L. K., Garg, M., & Srivastava, B. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1229805 · Cited by 38 · see citing works

Proactive Conversational Agents

Liao, L., Yang, G. H., & Shah, C. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3572724 · Cited by 20 · 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

SafeText: A Benchmark for Exploring Physical Safety in Language Models

Levy, S., Allaway, E., Subbiah, M., Chilton, L. B., Patton, D. U., McKeown, K. R., & Wang, W. Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.154 · Cited by 14 · 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

Conversational AI for multi-agent communication in Natural Language

Lemon, O. (2022) · AI Communications · doi:10.3233/aic-220147 · Cited by 13 · see citing works

A Benchmark for Understanding Dialogue Safety in Mental Health Support

Qiu, H., Zhao, T., Li, A., Zhang, S., He, H., & Lan, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44696-2_1 · Cited by 11 · see citing works

Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems

Gros, D., Li, Y., & Zhou, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.215 · Cited by 14 · see citing works

This prompt is measuring <mask>: evaluating bias evaluation in language models

Goldfarb-Tarrant, S., Ungless, E. L., Balkır, E., & Blodgett, S. L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.139 · Cited by 10 · 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

Computer says “No”: The Case Against Empathetic Conversational AI

Curry, A. C., & Curry, A. C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.515 · Cited by 9 · 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

Viewpoint: Artificial Intelligence Accidents Waiting to Happen?

Bianchi, F., Curry, A. C., & Hovy, D. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14263 · Cited by 7 · see citing works

NLP verification: towards a general methodology for certifying robustness

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Socially Aware Language Technologies: Perspectives and Practices

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Performance Evaluation of Large Language Models: A Comprehensive Review

Meva, D., & Kukadiya, H. (2025) · International Research Journal of Computer Science · doi:10.26562/irjcs.2025.v1203.03 · Cited by 6 · see citing works

Multitask Multimodal Prompted Training for Interactive Embodied Task Completion

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Walking a Tightrope – Evaluating Large Language Models in High-Risk Domains

Hung, C.-C., Rim, W. B., Frost, L., Bruckner, L., & Lawrence, C. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.8 · Cited by 5 · see citing works

Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI

Mei, A., Levy, S., & Wang, W. Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.701 · Cited by 3 · see citing works

Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue

Kim, D., Ahn, Y., Lee, C., Kim, W., Lee, K.-H., Shin, D.-H., & Lee, Y. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.252 · Cited by 2 · see citing works

Bootstrapping a Conversational Guide for Colonoscopy Prep

Arya, P., Bloomquist, M., Chakraborty, S., Perrault, A., Schuler, W., Fosler‐Lussier, E., & White, M. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.38 · Cited by 2 · see citing works

Mitigating Covertly Unsafe Text within Natural Language Systems

Mei, A., Kabir, A., Levy, S., Subbiah, M., Allaway, E., Judge, J., Patton, D. U., Bimber, B., McKeown, K. R., & Wang, W. Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.211 · Cited by 2 · see citing works

DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines

Gupta, P., Liu, Y., Jin, D., Hedayatnia, B., Gella, S., Liu, S., Lange, P. L., Hirschberg, J., & Hakkani-Tür, D. Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.935 · Cited by 1 · see citing works

I already said that! Degenerating redundant questions in open-domain dialogue systems.

Mai, L., & Carson-Berndsen, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-srw.33

Safety, Testing, and Maintenance

Reiter, E. (2024) · Natural Language Generation · doi:10.1007/978-3-031-68582-8_6

SafeServe: Scalable Tooling for Release Safety and Push Testing in Multi-App Monetization Platforms

Zhang, Y. (2025) · International Conference on Next Generation Data-driven Networks (NGDN) · doi:10.1109/ngdn66208.2025.11182150

Evaluation of AI ethics tools in language models: a developers' perspective case study

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Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety

Huang, W.-C., Zou, H. P., Wu, Y.-S., Li, D., Chen, Y., Zhang, W., Li, Y., Zangari, A., Guo, J., Miao, C., et al. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.2010

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models

Zhang, B., Gao, C., Yang, L., Han, B., Hu, M., Luo, Z., Geng, G., Bai, X., Zhang, J., Yao, W., et al. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.186 · Cited by 1 · see citing works

WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models

Gupta, P., Yau, L. Q., Low, H. H., Lee, I.-S., Lim, H. M., Teoh, Y. X., Hng, K. J., Liew, D. W., Bhardwaj, R., Bhardwaj, R., et al. (2024) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2024.emnlp-demo.42 · Cited by 7 · see citing works

SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs

Shi, Z., Jing, S., Cheng, Y., Zhang, H., Wang, Y., Zhang, J., Shen, H., & Cheng, X. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.85

Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models

Tan, Y., Zheng, B., Zheng, B., Cao, K., Jing, H., Wei, J., Liu, J., He, Y., Su, W., Zhu, X., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.732