GrounDial: Human-norm Grounded Safe Dialog Response Generation (2024.findings-eacl)
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| Challenge: | Recent conversational AI systems generate unsafe responses agreeing to offensive user input or including toxic content. |
| Approach: | They propose a method where response safety is achieved by grounding responses to commonsense social rules without fine-tuning. |
| Outcome: | The proposed approach is quantitatively and qualitatively safer even without additional data or tuning. |
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Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap
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Grounding Gaps in Language Model Generations (2024.naacl-long)
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| Challenge: | Effective conversation requires common ground, but it does not emerge spontaneously. |
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Grounding in social media: An approach to building a chit-chat dialogue model (2022.naacl-srw)
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SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures (2022.acl-long)
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| Challenge: | Existing open-domain conversational models can easily be made to talk in inadequate ways. |
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| Challenge: | Social norms govern culturally appropriate behavior in communication, enabling dialogue systems to produce coherent and socially acceptable outputs. |
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SafeConv: Explaining and Correcting Conversational Unsafe Behavior (2023.acl-long)
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| Challenge: | Existing datasets do not provide enough annotation to explain unsafe behavior . current chatbots generate toxic and offensive responses, which can be dangerous . |
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| Challenge: | despite progress toward data-driven conversational agents, dialogue models still suffer from issues surrounding safety and offensive language. |
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Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety (2025.findings-acl)
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DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
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