mlsanityData to 14 Oct 2026

Safer Conversational AI as a Source of User Delight

arXiv 2304.09865 ↗First posted 18 Apr 2023cs.HC cs.AIcited by 2

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Abstract (arXiv)

This work explores the impact of moderation on users' enjoyment of conversational AI systems. While recent advancements in Large Language Models (LLMs) have led to highly capable conversational AIs that are increasingly deployed in real-world settings, there is a growing concern over AI safety and the need to moderate systems to encourage safe language and prevent harm. However, some users argue that current approaches to moderation limit the technology, compromise free expression, and limit the value delivered by the technology. This study takes an unbiased stance and shows that moderation does not necessarily detract from user enjoyment. Heavy handed moderation does seem to have a nefarious effect, but models that are moderated to be safer can lead to a better user experience. By deploying various conversational AIs in the Chai platform, the study finds that user retention can increase with a level of moderation and safe system design. These results demonstrate the importance of appropriately defining safety in models in a way that is both responsible and focused on serving users.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 18 Apr 2023.

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27 of 43 references.

References · 27

  1. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  2. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  3. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  4. 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 722
  5. 2020Recipes for building an open-domain chatbotcited by 593
  6. 2020Towards a Human-like Open-Domain Chatbotcited by 470
  7. 2020GeDi: Generative Discriminator Guided Sequence Generationcited by 378
  8. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
  9. 2020Recipes for Safety in Open-domain Chatbotscited by 170
  10. 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
  11. 2020Multi-Dimensional Gender Bias Classificationcited by 80
  12. 2020PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learningcited by 79
15 more
  1. 2018Conversational AI: The Science Behind the Alexa Prizecited by 63
  2. 2018Polite Dialogue Generation Without Parallel Datacited by 59
  3. 2023Rewarding Chatbots for Real-World Engagement with Millions of Userscited by 59
  4. 2018Challenges for Toxic Comment Classification: An In-Depth Error Analysiscited by 56
  5. 2018Fighting Offensive Language on Social Media with Unsupervised Text Style Transfercited by 55
  6. 2020Controlling Style in Generated Dialoguecited by 38
  7. 2018A Unified Deep Learning Architecture for Abuse Detectioncited by 37
  8. 2022Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbotscited by 33
  9. 2020Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversationscited by 24
  10. 2017Detecting the Hate Code on Social Mediacited by 16
  11. 2019A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agentscited by 12
  12. 2018Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervisioncited by 6
  13. 2020Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Modelscited by 5
  14. 2020Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodologycited by 4
  15. 2022Grounding in social media: An approach to building a chit-chat dialogue modelcited by 4

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.