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FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

This is the implementation of our EMNLP 2023 paper FreeAL.

Title: FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

Authors: Ruixuan Xiao, Dong Yiwen, Junbo Zhao, Runze Wu, Minmin Lin, Gang Chen, Haobo Wang

Affliations: Zhejiang University, NetEase Fuxi AI Lab

Overview

In this paper, we overhaul the traditional active learning and propose a collaborative framework FreeAL, which employ LLMs as active annotators and SLMs as weak filters to distill task-related knowledge from LLMs. An overview of FreeAL can be seen as follows:

In each training loop, we alternate the following steps:

• Active labeling of the to-be-labeled samples via LLMs based on feedback from SLMs.
• Training weakly supervised SLMs to distill the task-related knowledge from the noisy annotations of LLMs and in turn feedback to them.

Requirements

After creating a virtual environment, run

pip install -r requirements.txt

Data preparation

We mainly evaluate our framework on sequence-level tasks and token-level tasks, including SST-2, MR, SUBJ, TREC, MA (Medical Abstract), CoNLL03, and BC5CDR datasets. All the datasets we used are publicly available datasets. The data for SLMs should be placed under the data-in folder with the following structure:

data-in
 |-- mr  # data for mr datasets
 |    |-- train.csv # vanilla training data (for transductive testing)
 |    |-- train_chatbt.csv  # training data with augmentations and LLM-generated-labels
 |    |-- dev.csv  
 |    |-- test.csv  # test data (for inductive testing)
 |    |-- ...
 |-- sst2  # data for sst2 dataset
 |    |-- ...
 |--subj  # data for subj dataset
 |    |-- ...

The backtranslation script is in backtrans.py

How to run

We put the source code of active labeling by LLMs and self-training of SLMs in folder active_labeling_llm and self_training_slm respectively. FreeAL employs iterative paradigm of training. In the initial and the subsequent training loops, the details of collaborative training are slightly different.

Active labeling of LLMs

First, configure your openai API key in the openai_api_config.yml file. Notably, we adopt openai-manager which leverages multiple API keys in parallel for accelerated inference. You can also opt out and utilize the vanilla official API sample. The original dataset and self-generated demonstrations with corresponding sample embeddings should be put in embedding folder, which can be downloaded from this link.

After that, simply run

python active_labeling_llm.py

for the initial round, and

python active_labeling_llm.py --refinery

for the subsequent rounds with the feedbacks in the folder ../self_training_slm/feedback.

Robust self-training of LLMs

Prior to execution, make sure that the data_in folder is configured with annotations from the last round of LLMs (this can be accomplished using simple script). Then just run the shell script

After that, simply run

CUDA_VISIBLE_DEVICES=0 bash run_roberta_mr.sh tranductive

for transductive setting, and

CUDA_VISIBLE_DEVICES=0 bash run_roberta_mr.sh inductive

for inductive setting. We will continue to improve the imperfections in our code!

How to cite

@inproceedings{xiao-etal-2023-freeal,
    title = "{F}ree{AL}: Towards Human-Free Active Learning in the Era of Large Language Models",
    author = "Xiao, Ruixuan  and
      Dong, Yiwen  and
      Zhao, Junbo  and
      Wu, Runze  and
      Lin, Minmin  and
      Chen, Gang  and
      Wang, Haobo",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.896",
    doi = "10.18653/v1/2023.emnlp-main.896",
    pages = "14520--14535"
}