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arXiv AI By Nelson Jo

Distributional Alignment of LLMs and Its Generalization Properties

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The paper investigates how large language models (LLMs) can be distributionally aligned with human output distributions, particularly for applications like survey simulation. It examines both the alignment process and the models’ ability to generalize to unseen questions within the same domain and to related but different topics. The study finds that while LLMs can achieve alignment and generalize within a domain, cross‑topic generalization is variable and aligned models may unexpectedly lose creative capabilities, indicating unpredictable and unstable generalization properties.

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