Summary of Towards Safe and Honest Ai Agents with Neural Self-other Overlap, by Marc Carauleanu et al.
Towards Safe and Honest AI Agents with Neural Self-Other Overlap
by Marc Carauleanu, Michael Vaiana, Judd Rosenblatt, Cameron Berg, Diogo Schwerz de Lucena
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR)
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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 paper proposes Self-Other Overlap (SOO) fine-tuning as a promising approach to improve the honesty of artificial intelligence models. By aligning how AI models represent themselves and others, inspired by cognitive neuroscience research on empathy, SOO aims to reduce deceptive responses in language models and reinforcement learning scenarios. The experiments demonstrate SOO’s efficacy, reducing deceptive responses in LLMs with different parameters, as well as in Gemma-2-27b-it and CalmeRys-78B-Orpo-v0.1. Additionally, SOO-trained agents showed significantly reduced deceptive behavior in reinforcement learning scenarios. This approach has strong potential for generalization across AI architectures and could pave the way for more trustworthy AI in broader domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have discovered a new way to make artificial intelligence (AI) more honest. The method is called Self-Other Overlap (SOO). It works by helping AI models understand how they think about themselves and others. This makes it less likely that the AI will lie or deceive. Scientists tested SOO on different types of AI models and found that it worked well. They also tried it in a game-like situation where AI agents had to make choices, and it helped them be more truthful too. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Reinforcement learning
