  Alignment with human preference prevents large language models (LLMs) from
generating misleading or toxic content while requiring high-cost human
feedback. Assuming resources of human annotation are limited, there are two
different ways of allocating considered: more diverse PROMPTS or more diverse
RESPONSES to be labeled. Nonetheless, a straightforward comparison between
their impact is absent. In this work, we first control the diversity of both
sides according to the number of samples for fine-tuning, which can directly
reflect their influence. We find that instead of numerous prompts, more
responses but fewer prompts better trigger LLMs for human alignment.
Additionally, the concept of diversity for prompts can be more complex than
responses that are typically quantified by single digits. Consequently, a new
formulation of prompt diversity is proposed, further implying a linear
correlation with the final performance of LLMs after fine-tuning. We also
leverage it on data augmentation and conduct experiments to show its effect on
different algorithms.
