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icl-selective-annotation

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[ICLR 2023] Code for our paper "Selective Annotation Makes Language Models Better Few-Shot Learners"

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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](http://arxiv.org/abs/2209.01975)

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},
}
```

icl-selective-annotation

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