Papers by Burcin Becerik-Gerber

    1 papers
    Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations (2025.emnlp-main)

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    Challenge: Language-driven generative agents have enabled large-scale social simulations with transformative uses, from interpersonal training to aiding global policy-making.
    Approach: They propose a framework for persona-environment Behavioral Alignment that iteratively refines agent personas and aligns them with real-world expert benchmarks.
    Outcome: The proposed framework greatly enhances behavioral realism and reliability in high-stakes social simulations.

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