@InProceedings{estienne:2023:RANLPStud,
  author    = {Estienne, Lautaro},
  title     = {Unsupervised Calibration through Prior Adaptation for Text Classification using Large Language Models},
  booktitle      = {Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing},
  month          = {September},
  year           = {2023},
  address        = {Varna, Bulgaria},
  publisher      = {INCOMA Ltd., Shoumen, Bulgaria},
  pages     = {13--22},
  abstract  = {A wide variety of natural language tasks are currently being addressed with large-scale language models (LLMs). These models are usually trained with a very large amount of unsupervised text data and adapted to perform a downstream natural language task using methods like fine-tuning, calibration or in-context learning. In this work, we propose an approach to adapt the prior class distribution to perform text classification tasks without the need for labelled samples and only a few in-domain sample queries. The proposed approach treats the LLM as a black box, adding a stage where the model posteriors are calibrated to the task. Results show that these methods outperform the un-adapted model for different number of training shots in the prompt and a previous approach where calibration is performed without using any adaptation data.},
  url       = {https://aclanthology.org/2023.ranlpstud-1.2}
}