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The field of artificial intelligence (AI) alignment aims to investigatewhether AI technologies align with human interests and values and function in asafe and ethical manner. AI alignment is particularly relevant for largelanguage models (LLMs), which have the potential to exhibit unintended behaviordue to their ability to learn and adapt in ways that are difficult to predict.In this paper, we discuss methodological challenges for the alignment problemspecifically in the context of LLMs trained to summarize texts. In particular,we focus on methods for collecting reliable human feedback on summaries totrain a reward model which in turn improves the summarization model. Weconclude by suggesting specific improvements in the experimental design ofalignment studies for LLMs' summarization capabilities.

Methodological reflections for AI alignment research using human feedback