Check out our preprint here: arxiv.org/abs/2411.10912
This paper was a collaborative effort from our wonderful team ❤️ @kjfeng.me @chanpark.bsky.social @axz.bsky.social

With SPICA, we show the need to not only capture preferences, but also recognize and prioritize norms when it comes to in-context pluralistic alignment.

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But, more importantly, groups that are often less well represented in alignment datasets see the biggest improvements.

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Through human evaluations, we find that SPICA-aligned outputs are preferred more on average…

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

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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)

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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.

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Traditional in-context alignment (ICA) retrieves demonstration examples (query & answer) by finding those most similar to a new query. However, when there is a plurality of groups to align to, the same queries get picked regardless of group.

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Next @cqz.bsky.social gave a talk on Wed on targeted interventions to reduce uncertainty in judgments. Paper here: dl.acm.org/doi/10.1145/... He also discussed how it fits into his broader research trajectory and agenda, as he's headed onto the job market this year!