  The field of artificial intelligence (AI) alignment aims to investigate
whether AI technologies align with human interests and values and function in a
safe and ethical manner. AI alignment is particularly relevant for large
language models (LLMs), which have the potential to exhibit unintended behavior
due to their ability to learn and adapt in ways that are difficult to predict.
In this paper, we discuss methodological challenges for the alignment problem
specifically in the context of LLMs trained to summarize texts. In particular,
we focus on methods for collecting reliable human feedback on summaries to
train a reward model which in turn improves the summarization model. We
conclude by suggesting specific improvements in the experimental design of
alignment studies for LLMs' summarization capabilities.
