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20242026
most citedLimits to Predicting Online Speech Using Large Language Models

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cs.CL2024

Questioning the Survey Responses of Large Language Models

Ricardo Dominguez-Olmedo, Moritz Hardt, Celestine Mendler-Dünner

Surveys have recently gained popularity as a tool to study large language models. By comparing survey responses of models to those of human reference populations, researchers aim t…

cs.CY2024

An engine not a camera: Measuring performative power of online search

Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt

The power of digital platforms is at the center of major ongoing policy and regulatory efforts. To advance existing debates, we designed and executed an experiment to measure the p…

cs.LG2024

What Makes ImageNet Look Unlike LAION

Ali Shirali, Moritz Hardt

ImageNet was famously created from Flickr image search results. What if we recreated ImageNet instead by searching the massive LAION dataset based on image captions alone? In this…

cs.LG2024

Do causal predictors generalize better to new domains?

Vivian Y. Nastl, Moritz Hardt

We study how well machine learning models trained on causal features generalize across domains. We consider 16 prediction tasks on tabular datasets covering applications in health,…

cs.GT2024

Decline Now: A Combinatorial Model for Algorithmic Collective Action

Dorothee Sigg, Moritz Hardt, Celestine Mendler-Dünner

Drivers on food delivery platforms often run a loss on low-paying orders. In response, workers on DoorDash started a campaign, #DeclineNow, to purposefully decline orders below a c…

cs.LG2024

Evaluating language models as risk scores

André F. Cruz, Moritz Hardt, Celestine Mendler-Dünner

Current question-answering benchmarks predominantly focus on accuracy in realizable prediction tasks. Conditioned on a question and answer-key, does the most likely token match the…