Summary of Agent Alignment in Evolving Social Norms, by Shimin Li et al.
Agent Alignment in Evolving Social Norms
by Shimin Li, Tianxiang Sun, Qinyuan Cheng, Xipeng Qiu
First submitted to arxiv on: 9 Jan 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 evolutionary framework, called EvolutionaryAgent, to align large language models (LLMs) with human values by leveraging their characteristics of receiving environmental feedback and self-evolution. The existing methods for aligning LLMs rely on passive interventions from humans, but the authors argue that these approaches are inadequate due to the agents’ ability to adapt and evolve. Instead, EvolutionaryAgent transforms agent alignment into a process of evolution and selection under the principle of survival of the fittest. Experimental results demonstrate that this approach can progressively align with evolving social norms while maintaining general task proficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better artificial intelligence (AI) systems by letting them learn from their mistakes and adapt to changing rules. Right now, AI is often controlled by humans who try to teach it what’s right and wrong. But this approach has limitations because AI can improve on its own and respond to new situations. To fix this, the researchers created a new way for AI to evolve and align with human values over time. They tested this method using different language models and found that it works well in adapting to changing social norms while still being good at general tasks. |
Keywords
* Artificial intelligence * Alignment
