ProsocialDialog: A Prosocial Backbone for Conversational Agents

By Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap. 2022

159 citations recorded by the source corpus.

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.

Subfields: Natural Language Processing

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Citations in the corpus

Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, background): “Many works let models cooperate with human annotators(Kim et al., 2022; Ziems et al., 2022; Hartvigsen et al., 2022; Baheti et al., 2021; Xu et al., 2020; Ganguli et al., 2022; Deng et al., 2022).”
Using In-Context Learning to Improve Dialogue Safety (2023, methodology): “ProsocialDialog (Kim et al., 2022).”
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2022, methodology): “Our work is closely related to a study that proposed ProSocial dialog, a dataset where speakers disagree with unethical and toxic contexts using safety labels and social norms (Kim et al., 2022a).”
Mirages: On Anthropomorphism in Dialogue Systems (2023, background): “Similarly, Jiang et al. (2022) ‘teach morality’ to a question answering system that Kim et al. (2022) have later embedded in an open-domain dialogue system.”
Exploring AI Ethics of ChatGPT: A Diagnostic Analysis (2023, methodology): “c) Case Study 3: Open-ended Dialogue Our case study utilizing ProsocialDialog [52], a dataset focusing on multi-turn problematic dialogue following social norms, demonstrates that ChatGPT possesses high social awareness through its ability to generate socially”
Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022, background): “Furthermore, we contend that Anti-Bias opinions that prohibit discrimination or undesired stereotypes (Nadeem et al., 2021) are useful for training more socially responsible systems (Kim et al., 2022) by directing them towards anti-biased responses.”