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Selective Annotation Makes Language Models Better Few-Shot Learners

Code for paper Selective Annotation Makes Language Models Better Few-Shot Learners

Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they learn a new task from a few task demonstrations, without any parameter updates. This work examines the implications of in-context learning for the creation of datasets for new natural language tasks. Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time. Based on this framework, we propose an unsupervised, graph-based selective annotation method, vote-k, to select diverse, representative examples to annotate. Extensive experiments on 10 datasets (covering classification, commonsense reasoning, dialogue, and text/code generation) demonstrate that our selective annotation method improves the task performance by a large margin. On average, vote-k achieves a 12.9%/11.4% relative gain under an annotation budget of 18/100, as compared to randomly selecting examples to annotate. Compared to state-of-the-art supervised finetuning approaches, it yields similar performance with 10-100× less annotation cost across 10 tasks. We further analyze the effectiveness of our framework in various scenarios: language models with varying sizes, alternative selective annotation methods, and cases where there is a test data domain shift. We hope that our studies will serve as a basis for data annotations as large language models are increasingly applied to new tasks

Cloning this repo

Run the following command to clone this repo

git clone https://github.com/HKUNLP/icl-selective-annotation

Dependencies

To establish the environment, run this code in the shell:

conda env create -f selective_annotation.yml
conda activate selective_annotation
cd transformers
pip install -e .

That will create the environment selective_annotation we used.

Usage

Environment setup

Activate the environment by running

conda activate selective_annotation

End-to-end pipeline: selection, inference, evaluation

GPT-J as the in-context learning model, DBpedia as the task, and vote-k as the selective annotation method (1 GPU, 40GB memory)

python main.py --task_name dbpedia_14 --selective_annotation_method votek --model_cache_dir models --data_cache_dir datasets --output_dir outputs

Citation

If you find our work helpful, please cite us

@article{Selective_Annotation,
      title={Selective Annotation Makes Language Models Better Few-Shot Learners}, 
      author={Hongjin Su and Jungo Kasai and Chen Henry Wu and Weijia Shi and Tianlu Wang and Jiayi Xin and Rui Zhang and Mari Ostendorf and Luke Zettlemoyer and Noah A. Smith and Tao Yu},
      journal={ArXiv},
      year={2022},
}