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INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed. In practice, however, data are often collected based on convenience, availability, or cost, resulting in systematic sampling biases that can distort
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