lyyf2002 / llmaaa

The official repository for paper "LLMaAA: Making Large Language Models as Active Annotators"

LLMaAA

The official repository for paper "LLMaAA: Making Large Language Models as Active Annotators".

Key Dependencies

• openai 0.27.4

• numpy 1.24.2

• torch 1.10.0+cu111

• tokenizers 0.10.3

• transformers 4.12.4

• sentence-transformers 2.2.2

• kmeans-pytorch 0.3

• func_timeout

• ujson

• tqdm

Brief Introduction to usage

Disclaimer: Since I (the first author) didn't have access to Azure OpenAI service after internship at Langboat, I haven't test the code recently. So unfortunately I cannot guarantee that the code can be ran bug-free without any modification and please take this repository as a reference implementation.

1. Setup openai config @ ~/openai_config.json. We use the Azure GPT API in our experiments, so in default you need to provide the key and base for OpenAI service.

2. Download data to ~/data/. See ~/data/README.md in the directory for details.

3. For active annotation (LLMaAA),

   • First retrieve demonstations (random/knn) for train/test data with ~/src/demo_retrieval.py.
   • Run active annotation with ~/src/active_annotate.py. Since the demo indices are static, so the previous annotation results will be stored in an auto-generated cache file.

4. For data generation (ZeroGen/FewGen),

   • Run with ~/src/data_gen.py.

5. For testing prompting (Prompt) performance directly,

   • Run inference and evaluation with ~/src/llm_test.py. May experience timeout/ratelimit/etc.

Adapt to new dataset

1. Prepare the data in NER/RE format.
2. Setup meta.json in data directory and configs/{dataset}.json for annotator/generator. The configs folder can be found in ~/src/data_synth/ (for generator) and ~/src/llm_annotator (for annotator).
3. If use demonstration in prompt engineering, you need to build a map from train/test to demo data, with ~/src/demo_retrieval.py.

Citation

@inproceedings{zhang-etal-2023-llmaaa,
    title = "{LLM}a{AA}: Making Large Language Models as Active Annotators",
    author = "Zhang, Ruoyu  and Li, Yanzeng  and Ma, Yongliang  and Zhou, Ming  and Zou, Lei",
    editor = "Bouamor, Houda  and Pino, Juan  and Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.872",
    doi = "10.18653/v1/2023.findings-emnlp.872",
    pages = "13088--13103",
}