The benefits, risks and bounds of personalizing the alignment of large language models to individuals

Abstract

Large language models (LLMs) undergo ‘alignment’ so that they better reflect human values or preferences, and are safer or more useful. However, alignment is intrinsically difficult because the hundreds of millions of people who now interact with LLMs have different preferences for language and conversational norms, operate under disparate value systems and hold diverse political beliefs. Typically, few developers or researchers dictate alignment norms, risking the exclusion or under-representation of various groups. Personalization is a new frontier in LLM development, whereby models are tailored to individuals. In principle, this could minimize cultural hegemony, enhance usefulness and broaden access. However, unbounded personalization poses risks such as large-scale profiling, privacy infringement, bias reinforcement and exploitation of the vulnerable. Defining the bounds of responsible and socially acceptable personalization is a non-trivial task beset with normative challenges. This article explores ‘personalized alignment’, whereby LLMs adapt to user-specific data, and highlights recent shifts in the LLM ecosystem towards a greater degree of personalization. Our main contribution explores the potential impact of personalized LLMs via a taxonomy of risks and benefits for individuals and society at large. We lastly discuss a key open question: what are appropriate bounds of personalization and who decides? Answering this normative question enables users to benefit from personalized alignment while safeguarding against harmful impacts for individuals and society.