An AI development team has fine-tuned a large language model primarily to follow user commands. The model excels at tasks with clear, explicit instructions (e.g., 'Summarize this article in three bullet points'). However, for more open-ended prompts (e.g., 'Explain quantum computing in a simple way'), its responses are often factually correct but overly technical, verbose, and not genuinely helpful for a layperson. Which of the following strategies best addresses this specific shortcoming by building upon the model's existing capabilities?

Analyzing LLM Alignment Contributions

A development team is creating a new large language model using a two-stage alignment process. First, they train the model to follow a wide range of commands. Second, they refine the model to ensure its responses are helpful, harmless, and honest. Match each desired model behavior below to the alignment stage that is primarily responsible for achieving it.

Preference Models as a Sequential Step for Generalization