To align the output of Large Language Models (LLMs) with users’ values and expectations, a human-in-the-loop feedback process is essential. This process involves a team of evaluators who review the model’s output across various tasks and use cases, providing rankings based on criteria like helpfulness, fairness, and clarity. These rankings can range from “best” to “worst” according to company policies, offering insight into the model’s strengths and weaknesses.
Meta’s LLaMA model uses a technique called Reinforcement Learning from Human Feedback (RLHF), which they describe as “red teaming.” This process identifies model vulnerabilities and risks by crafting prompts that might provoke undesirable behavior or outputs. It is a proactive way to test the model’s safeguards and resilience, attempting to “jailbreak” it by challenging its boundaries.
To improve the safety and reliability of LLMs, red teaming requires diversity, subject-matter expertise, and frequent testing. RLHF aims to integrate safeguards directly into the model’s design, reducing risks associated with harmful outputs. However, analyzing the real-world context behind LLM responses, especially concerning metaphors and cultural nuances, remains challenging. This is where creative research and imaginative thinking become vital in identifying potential risks.
Moreover, it’s crucial to avoid relying on low-paid workers for this feedback process. The ideal scenario involves a well-compensated, diverse, and collaborative team of experts who can work together to design tests and assess outputs effectively, ensuring thorough risk analysis and ethical alignment.
Source Llama “Responsible Use Guide” from Meta