LLM 分布对齐及其泛化性质研究
Distributional Alignment of LLMs and Its Generalization Properties
AI 导读
一项发表于 IJCAI 2026 Augmented Democracies Workshop 的研究探讨如何让大语言模型实现分布对齐，即让输出分布匹配人类参考分布，以及这种对齐能否泛化到未见样本和无关话题。
正文 · 原文
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)
来源：arXiv cs.AI · arxiv.org