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qa-from-hf

Code for Continually Improving Extractive QA via Human Feedback. Please contact the first authors by email if you have any question.

Table of Contents

Basics

Brief intro to each folder and file at the root:

  1. data-collection/: Examples and qualification tests designed for our user study.
  2. data/: All the data we collected for both the long-term study and analysis on model variants. You could use these data to reproduce our results.
  3. scripts/: Example scripts for training and testing the models.
  4. src/: data.py is the script for loading the data; eval.py is the script for evaluation.
  5. src_analysis/: Scripts for analyzing the results.
  6. src_utils/: Miscellaneous utility functions.
  7. generate_prob.py: The script we used to store the generation probability in the data files.
  8. random_indices_squad2.txt: The random indices we use to shuffle the SQuAD2.0 initial data. Will need this file to reproduce our initial model.
  9. model.py: Script for model defination.
  10. train_bandit.py: Training script for bandit learning.
  11. train_initial.py: Training script for initial model training.

Data

We are using squad_v2 dataset on Hugging Face for SQuAD2-initialized models.
We use the NewsQA data from TODO: this_link.

You can find all other data used in our paper in the data folder:

Installation

  1. This project is developed in Python 3.6. Using Conda to set up a virtual environment is recommended.

  2. Install the required dependencies.

    pip install -r requirements.txt

  3. Install PyTorch from http://pytorch.org/.

Reproduction

Initial Training

We train an initial DeBERTaV3 model on a set of random sampled 512 SQuAD2 examples, or on NewsQA.

Bandit Learning

We iteratively improve the model via multiple rounds of user interaction. At each round, the pipeline is to specify the feedback data for training, and then conduct the bandit learning. Concrete steps are as follows:

  1. Specifiy Training Data: Before each round of bandit learning, you should specify the training data by modifying train_files.txt. To do so, you could simply run src_utils/write_data_file.py with corresponding arguments.

An example script for long-term experiments:

  python src_utils/write_data_file.py --exp long-term --r_idx 1

An example script for experiments on different model variants:

  python src_utils/write_data_file.py --exp variants  --r_idx 1 --variant fewer

fewer for fewer examples per round, default for default setup, newsqa for domain adaptation from NewsQA, noclass for ablation on classification head, and weaker for starting with a weaker initial model.

  1. Training: Run train_bandit.py to do bandit learning. We perform hyperparameter tuning on num_train_epochs, learning_rate and entropy_coeff as mentioned in the paper.
    An example script is provided below: (refer to scripts/train_bandit.sh for more details)

     python train_bandit.py   --do_train  \
                       --do_eval   \
                       --train_file train_files.txt   \
                       --output_dir [your_output_dir]   \
                       --initialize_model_from_checkpoint [your_model_path]   \
                       --checkpoint_name [your_model_name]  \
                       --dev_file data/Dev-400.jsonl.gz   \
                       --num_train_epochs 30   \
                       --learning_rate 3e-6   \
                       --entropy_coeff 5.0   \
                       --train_batch_size 35   \
                       --version_2_with_negative   \
                       --prepend_title   \
                       --load_log_prob   \
                       --tag example \
                       --round_index 1   \
                       --turn_off_dropout   \
                       --add_classifier   \
                       --rehearsal

You should specify output_dir which is the the output directory (for storing the model and training log) and, initialize_model_from_checkpoint and checkpoint_name which are the path to and name of the model that you want to start with. For Round 1, this model path should be that of an initial model obtrained from inital training. For ablation on classification head, remeber to remove --add_classifier.

For the next round of bandit learning, repeat the above 2 steps. At every round, remember to change initialize_model_from_checkpoint in step 2 to be the best-performing model on the development set from the previous round.

Evaluation

First, you need to specify which datasets/files you would like to evaluate your model on. You can modify test_files.txt to indicate which files you would like to test on. Each line represents a test file, and should be formatted as [feedback type]\t[file name]. The example test_files.txt in the repo lists all possible datasets that you can evaluate on.

To conduct the evaluation, run train_bandit.py with proper arguments: output_dir which is the the output directory for evaluation results, initialize_model_from_checkpoint and checkpoint_name which are the path to and name of the model that you want to evaluate. An example script is as follows: (refer to scripts/test.sh for more details)

  python train_bandit.py \
        --do_eval \
        --eval_test \
        --model microsoft/deberta-v3-base \
        --test_file data/test_files.txt \
        --output_dir  [your_output_dir] \
        --initialize_model_from_checkpoint [your_model_path]  \
        --checkpoint_name [your_model_name]  \
        --version_2_with_negative \
        --prepend_title \
        --add_classifier

The results of the evaluation will be stored at the specified output_dir and printed as standard output.

Citation

  @InProceedings{Gao23continually,
  author    = {Ge Gao, Hung-Ting Chen, Yoav Artzi, and Eunsol Choi},
  title     = {Continually Improving Extractive QA via Human Feedback},
  booktitle = {EMNLP},
  year      = {2023}
  }