webis/naacl25-prompt-compositions_finetune-baseline

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection

Finetune baseline models for the paper Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection.

For details, please see the published paper and the GitHub repository.

@inproceedings{spliethover-etal-2025-adaptive,
    title =      "Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection",
    author =     Splieth{\"o}ver, Maximilian and Knebler, Tim and Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and H{\"u}llermeier, Eyke and Wachsmuth, Henning,
    booktitle =  "Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics",
    month =      apr,
    year =       "2025",
    address =    "Albuquerque, New Mexico",
    publisher =  "Association for Computational Linguistics",
    url =        "https://arxiv.org/abs/2502.06487",
}

Note on finetune baseline models

Unfortunately, we did not keep the original finetuning baseline models, for which scores are reported in the paper. We did, however, keep the prediction results of these models.

We did retrain the models on the same splits, same seeds, same python version, and same library versions. The new models and also the new (and old) prediction results are uploaded in this repository.