Today, I explored resources related to Anthropic’s research on persona vectors as part of my AI safety studies. Below are the resources I reviewed.
Resource: Persona Vectors: Monitoring and Controlling Character Traits in Language Models
- Source: Persona Vectors: Monitoring and Controlling Character Traits in Language Models , Anthropic Research; related paper: Persona Vectors: Monitoring and Controlling Character Traits in Language Models by Runjin Chen et al.; implementation: GitHub - safety-research/persona_vectors .
- Summary: This Anthropic Research page introduces persona vectors, patterns of neural network activity in large language models (LLMs) that control character traits like evil, sycophancy, or hallucination. The associated paper details a method to extract these vectors by comparing model activations for opposing behaviors (e.g., evil vs. non-evil responses). Persona vectors enable monitoring of personality shifts during conversations or training, mitigating undesirable traits through steering techniques, and flagging problematic training data. The method is tested on open-source models like Qwen 2.5-7B-Instruct and Llama-3.1-8B-Instruct. The GitHub repository provides code for generating persona vectors, evaluating their effectiveness, and applying steering during training to prevent unwanted trait shifts, offering tools for maintaining alignment with human values.