Papers by Ofer Meshi

1 papers
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders (2026.eacl-long)

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Challenge: a "realism gap" exists between simulations and real-world user models . large language models (LLMs) are a key component of conversational AI .
Approach: They propose a framework that combines statistical alignment, human-likeness score and counterfactual validation to test for generalization.
Outcome: The proposed framework outperforms baselines in counterfactual validation, showing that data-driven simulators adapt more realistically to unseen behaviors.

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