Summary of Adversarial Dpo: Harnessing Harmful Data For Reducing Toxicity with Minimal Impact on Coherence and Evasiveness in Dialogue Agents, by San Kim et al.
Adversarial DPO: Harnessing Harmful Data for Reducing Toxicity with Minimal Impact on Coherence and Evasiveness in Dialogue Agents
by San Kim, Gary Geunbae Lee
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes an innovative training algorithm for open-domain dialogue systems called adversarial direct preference optimization (ADPO). Building on recent advancements in large language models and effective training methodologies, ADPO aims to train models to assign higher probabilities to preferred responses and lower probabilities to unsafe responses. The algorithm is designed to enhance the model’s resilience against harmful conversations while minimizing performance degradation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special training method for chatbots that can understand when someone is saying something bad or mean. It’s called ADPO, which stands for Adversarial Direct Preference Optimization. Think of it like teaching a kid not to be mean by showing them what kind of behavior is not okay. The algorithm helps the chatbot learn what makes sense and what doesn’t, making sure people have a better experience when they talk to it. |
Keywords
» Artificial intelligence » Optimization
