Large language models (LLMs) aligning with human expectations is crucial for societal benefits. Reinforcement learning from human feedback (RLHF) and direct alignment from preferences (DAP) are approaches discussed. A new study introduces Online AI Feedback (OAIF) for DAP, combining online flexibility and efficiency. Empirical comparisons demonstrate OAIF’s effectiveness, especially in aligning LLMs online.
Maximizing Societal Advantages with AI Alignment
Aligning large language models (LLMs) with human expectations and values is crucial for maximizing societal advantages.
Approaches to AI Alignment
Reinforcement learning from human feedback (RLHF) and direct alignment from preferences (DAP) are two key approaches to AI alignment.
Challenges and Solutions
DAP approaches use preference datasets, but they typically only provide offline feedback. To address this, Online AI Feedback (OAIF) for DAP techniques has been proposed, combining the online flexibility of RLHF with the efficiency of DAP methods.
With OAIF, a three-step process is followed to align an LLM policy:
- Two responses from the existing policy are chosen at random.
- An LLM is instructed to imitate human preference annotation to gather online feedback over the two responses.
- The model is updated using this online feedback using typical DAP losses.
Effectiveness of OAIF
Empirical comparisons demonstrate the efficacy of OAIF, showing that online DAP approaches outperform their offline counterparts by an average of 66% in human evaluation. The aligned policy’s average response length is reduced by 66% without sacrificing quality, showcasing the practical value of OAIF.
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