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Stress-Testing Large Language Models (LLMs) against Code-Mixing and Distributional Shifts in Low-Resource NLP

Domaine:

natural language processing

Type de record:

paper
Créateur:
Zaf
Éditeur:
Zenodo
Hôte:avatar
Initial public release accompanying the manuscript: Stress-Testing Large Language Models (LLMs) against Code-Mixing and Distributional Shifts in Low-Resource NLP Contents: Experimental code Results Figures Appendix figures Statistical significance testing Robustness evaluation Cross-domain generalization analysis If you use this repository, please cite the associated publication.

Visit

doi.org

Tasks

code switchinglanguage modeling

Licenses

MIT Licensehttps://opensource.org/licenses/MIT

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