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cre-dpsign

fedpetuning

glue-x

We leverage 14 datasets as OOD test data and conduct evaluations on 8 NLU tasks over 21 popularly used models. Our findings confirm that the OOD accuracy in NLP tasks needs to be paid more attention to since the significant performance decay compared to ID accuracy has been found in all settings.

ood_nlp

[NeurIPS 2023 D&B Track] Code and data for paper "Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations".

robust_weight_signatures

[ICML 2023] "Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?" by Ruisi Cai, Zhenyu Zhang, Zhangyang Wang

smart-pytorch

PyTorch – SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models.

task_vectors

Editing Models with Task Arithmetic

test

visual-graph-memory