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In-context learning can be an effective way to conduct value alignment of LLMs through examples, but when there are multiple pluralistic groups, are the best examples for one group also the ones for another? We explore this in our paper 🌟SPICA🌟 (🧵1/9)

We argue that group level differences extend beyond their preferences for how to answer, and that different groups can also have preferences around which queries are better examples of how they prioritize their values. (3/9)

In SPICA, we sample **individual preferences** of members in a group to create metrics inspired by social norm theory that inform us of how each group prioritizes which examples they care more about (best illustrates group norms) (4/9)

We then make use of these metrics during the retrieval process, producing pluralistically aligned examples that both reflect group preferences, and also their norms. (5/9)