Summary of Groundial: Human-norm Grounded Safe Dialog Response Generation, by Siwon Kim et al.
GrounDial: Human-norm Grounded Safe Dialog Response Generation
by Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi, Sungroh Yoon
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper, GrounDial, addresses the issue of unsafe responses generated by current conversational AI systems based on large language models (LLMs). While previous research has fine-tuned LLMs with manually annotated safe dialogue histories to alleviate toxicity, this approach requires substantial costs. Instead, GrounDial uses a hybrid approach that grounds responses to commonsense social rules without requiring additional data or tuning. This allows for quantitatively and qualitatively safer responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GrounDial is a new way to make conversational AI systems safer by using common sense and social rules, without needing extra training or data. Right now, these systems can sometimes respond in harmful ways when users ask them to be mean or offensive. To fix this, some research has used special training sets of safe conversations. But that takes a lot of time and money. GrounDial is different because it doesn’t need any additional training – it just uses what’s already there. |
