GrounDial: Human-norm Grounded Safe Dialog Response Generation

• URL: http://arxiv.org/abs/2402.08968v1
• Date: Wed, 14 Feb 2024 06:25:50 GMT
• Title: GrounDial: Human-norm Grounded Safe Dialog Response Generation
• Authors: Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi, and Sungroh Yoon
• Abstract summary: We propose GrounDial, where response safety is achieved by grounding responses to commonsense social rules without requiring fine-tuning. A hybrid approach of in-context learning and human-norm-guided decoding of GrounDial enables the response to be quantitatively and qualitatively safer even without additional data or tuning.
• Score: 39.55597493155821
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses, agreeing to offensive user input or including toxic content. Previous research aimed to alleviate the toxicity, by fine-tuning LLM with manually annotated safe dialogue histories. However, the dependency on additional tuning requires substantial costs. To remove the dependency, we propose GrounDial, where response safety is achieved by grounding responses to commonsense social rules without requiring fine-tuning. A hybrid approach of in-context learning and human-norm-guided decoding of GrounDial enables the response to be quantitatively and qualitatively safer even without additional data or tuning.

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