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Distributional Alignment of LLMs and Its Generalization Properties

Summary

An arXiv paper presented at the IJCAI 2026 Workshop on Augmented Democracies studies distributional alignment in large language models: how to make model output distributions match a human reference distribution, and how aligned models generalize to unseen samples, topics, and tasks. The work finds same-domain generalization is achievable, cross-topic generalization depends on topic relationships, and alignment can cause surprising degradations, making generalization inherently unpredictable and unstable.

SourcearXiv Computational LinguisticsAuthor: Nelson Jo
Distributional Alignment of LLMs and Its Generalization Properties
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[Submitted on 5 Oct 2026]

Title:Distributional Alignment of LLMs and Its Generalization Properties

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Abstract:Large language models are increasingly used to simulate and probe human behavior, e.g., for surveys. For these applications, a key property is that outputs satisfy distributional alignment: to match its output distribution with some reference (in this case, human) distribution. An obstacle in achieving distributional alignment is generalizing from small training data. In this work, we study (1) how to distributionally align LLMs; and (2) how aligned LLMs might generalize to unseen samples and unrelated topics and tasks. Using two large-scale benchmarks, we find that LLMs can indeed achieve distributional alignment and generalize to unseen questions of the same domain. Cross-topic generalization depends on the relationship between topics (e.g., questions about politics are related to trust in institutions). However, aligned LLMs can degrade in surprising ways (e.g., lose their creative ability), suggesting that its generalization properties are inherently unpredictable and unstable.

Comments: IJCAI 2026 Workshop on Augmented Democracies

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.31687 [cs.CL]

(or arXiv:2610.31687v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.31687

arXiv-issued DOI via DataCite

Submission history

From: Nelson Jo [view email] [v1] Mon, 5 Oct 2026 12:13:18 UTC (646 KB)

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Key points

  • The study examines how to distributionally align LLMs so their outputs match a human reference distribution, and how aligned models generalize.
  • Using two large-scale benchmarks, LLMs achieve distributional alignment and generalize to unseen questions within the same domain; cross-topic generalization depends on relationships between topics.
  • Aligned LLMs can degrade in surprising ways, such as losing creative ability, indicating that generalization properties are inherently unpredictable and unstable.

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