This model is released as part of the paper:
Cross-Prompt Encoder for Low-Performing Languages
Findings of IJCNLP–AACL 2025; preprint at arXiv:2508.10352.

The paper studies cross-lingual transfer learning for low-performing languages using parameter-efficient, prompt-based methods on the SIB-200 benchmark.

This repository provides the trained Cross-Prompt Encoder (XPE) adapter used in the study. It is a parameter-efficient soft-prompt model designed to be loaded on top of a frozen XLM-R (large) backbone, and contains the learned:

Soft Prompt
Prompt Encoder
Classification Head

Model Details

Adaptation: Parameter-Efficient Fine-Tuning (PEFT), Cross-Prompt Encoder (XPE)
Backbone: FacebookAI/xlm-roberta-large
Task: Multilingual Topic Classification
Benchmark: Davlan/sib200
Source Language Group: XLM-R Seen Languages

Seeds

This repository includes 10 models trained with different random seeds.
The main branch corresponds to seed-01
Models for other seeds are available as branches: seed-02, seed-03, …, seed-10

Usage

This model is part of the experimental framework introduced in the paper and is intended to be loaded and used via its canonical codebase.

Related Resources

Paper Preprint: Cross-Prompt Encoder for Low-Performing Languages
Code Repository: bmikaberidze/XPE
Benchmark: Davlan/sib200
Preprocessed Dataset: mikaberidze/sib200-xlmr-tokenized
Related Models:

Citation

If you use this model, please cite:

bibtex

@misc{mikaberidze2025crosspromptencoderlowperforminglanguages,
  title         = {Cross-Prompt Encoder for Low-Performing Languages},
  author        = {Beso Mikaberidze and Teimuraz Saghinadze and Simon Ostermann and Philipp Muller},
  year          = {2025},
  eprint        = {2508.10352},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2508.10352},
}

Contact

Beso Mikaberidze — beso.mikaberidze@gmail.com
Philipp Muller — mueller@is.mpg.de

mikaberidze/xlmr-large-sib200-peft-xpe-seen

Cross-Prompt Encoder (XPE) for XLM-R (large) — SIB-200