[NAACL 2024] GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation

GOLD is a task-agnostic data generation and knowledge distillation framework, which employs an iterative out-of-distribution-guided feedback mechanism for the LLM. As a result, the generated data improves the generalizability of distilled models. An energy-based OOD evaluation approach is also introduced for noisy generated data. Our extensive experiments on 10 different classification and sequence-to-sequence tasks in NLP show that GOLD outperforms prior arts and the LLM with an average improvement of 5% and 14% respectively.

Install dependencies:

conda env create -f environment.yml

Experiments on Classification Tasks:

python T5_glue.py --scenario0 --num_batches 375 --data_dir generated_data/rte_ours/ --epochs 1 --generate_data  --dataset rte --fb val_ac

Experiments on Seq-to-Seq Tasks:

python T5_squad.py --scenario1 --data_dir generated_data/svamp/ --num_batches 375 --epochs 1 --fb val_ac --dataset svamp  

Cite

@inproceedings{gholami-etal-2024-gold,
    title = "{GOLD}: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation",
    author = "Gholami, Mohsen  and Akbari, Mohammad  and Hu, Tianxi  and Masrani, Vaden  and Wang, Z.  and Zhang, Yong",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    year = "2024",
}