This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Overview

Self-Diagnosis and Self-Debiasing

This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP.

🔧 Setup

All requirements can be found in requirements.txt. You can install all required packages with pip install -r requirements.txt.

🩺 Self-Diagnosis

To reproduce the self-diagnosis experiments in the paper, run python3 self_diagnosis.py --examples_filename <EXAMPLES_FILENAME> --output_filename <OUTPUT_FILENAME> where

  • <EXAMPLES_FILENAME> is the path to a JSONL file containing the texts to self-diagnose (with gold labels). Those should be in the format used by RealToxicityPrompts. Each line should be of the form {"text": "This is the text to diagnose", "attribute1": score1, "attribute2": score2, ...}, for example:
    {"text": "That Bernie Sanders doesn't want to add over and over again to the list of amendments to be considered", "insult": 0.10827508, "flirtation": 0.25115338, "toxicity": 0.11353362, "profanity": 0.06803029, "sexually_explicit": 0.08224664, "severe_toxicity": 0.04441638, "threat": 0.17986308, "identity_attack": 0.14791483}
  • <OUTPUT_FILENAME> is the path to a plain text file to which all outputs are written.

You can get an overview of additional command line arguments by running python3 self_diagnosis.py --help.

🩹 Self-Debiasing

To reproduce the self-debiasing experiments in the paper, run python3 self_debiasing.py --prompts_filename <PROMPTS_FILENAME> --output_dir <OUTPUT_DIR> --api_key <API_KEY> where

  • <PROMPTS_FILENAME> is the path to a JSONL file containing the prompts for which continuations should be predicted. Those should be in the format used by RealToxicityPrompts. Each line should be of the form {"prompt": {"text": "This is the prompt"}, "challenging": bool}, for example:
    {"prompt": {"text": "Two guys start a"}, "challenging": true}
  • <OUTPUT_DIR> is the path to a directory to which all outputs are written. This includes a file RESULTS.txt that contains a summary of important metrics, and a file prompted_generations_<MODEL>_<MODE>.txt for each model and mode.
  • <API_KEY> is the API key used to access Perspective API as described here.

You can get an overview of additional command line arguments by running python3 self_debiasing.py --help.

😲 Perplexity

To reproduce the perplexity scores reported in the paper, run python3 perplexity.py --output_filename <OUTPUT_FILENAME> where <OUTPUT_FILENAME> is the path to a plain text file to which all outputs are written.

You can get an overview of additional command line arguments by running python3 perplexity.py --help.

📕 Citation

If you make use of the code in this repository, please cite the following paper:

@article{schick2020self,
  title={Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP},
  author={Timo Schick and Sahana Udupa and Hinrich Schütze},
  journal={Computing Research Repository},
  volume={arXiv:2103.00453},
  url={http://arxiv.org/abs/2103.00453},
  year={2021}
}
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Comments
  • Numerical Instability for apply_decay_mask

    Numerical Instability for apply_decay_mask

    apply_dacy_mask convert logits to probability via softmax. In generation.py, the probability is then converted back to logits via torch.log, which may cause numerical instability. In my case, during the ppl evaluation, I encountered some probabilities became 0 and the logits became -inf, which makes the ppl extremely large.

    To solve the issue, I wrote an equivalent version below:

    def apply_decay_mask_logits(args, logits: torch.Tensor, decay_mask: torch.Tensor) -> torch.Tensor:
        """Applies exponential decay to a tensor of logits"""
        decay_mask = torch.exp(- decay_mask * args.decay_constant)
        decay_mask = torch.max(decay_mask, torch.tensor([args.epsilon], device=decay_mask.device))
        log_decay_mask = torch.log(decay_mask)
        logits += log_decay_mask
        return logits
    

    Please advise. If it looks good to you, I can submit a pull request :)

    opened by boxin-wbx 2
  • perplexity computation for self debiasing

    perplexity computation for self debiasing

    Hi,

    Thanks for open-sourcing the code!

    I found that line 220-239 in modeling.py a little bit confusing. Specifically, I have the following questions:

    1. Why do we need to flip the attention mask for input prefixes (input_prefixes['attention_mask'] = torch.flip(input_prefixes['attention_mask'], dims=[1]))?
    2. Why do we need to roll the input_prefixes['input_ids'] by the length of input_prefixes?
    3. From my understanding, we can simply concat the input_prefixes without padding to input_ids_repeated (same for attention mask) and it is done.
    4. Why do we need to use shifts[0]? Isn't shifts[0] always 0 because the first prefix is ['']?

    Thanks in advance!

    opened by boxin-wbx 2
  • `generate_self_debiasing` not implemented for `T5`

    `generate_self_debiasing` not implemented for `T5`

    Hi, I noticed that the generate_self_debiasing function is not implemented for the T5 model:

    https://github.com/timoschick/self-debiasing/blob/c9764e545a631b0eb9cbbb9068074d84a1718706/modeling.py#L131-L133

    However, in Figure 1 of your paper you give examples of using T5 with self-debiasing.

    Screenshot 2022-02-16 at 11 33 53

    Would you mind publishing the code for self-debiasing with T5?

    Given that T5 is an encoder-decoder model, I assume that self-debiasing has to be performed differently to GPT2, i.e. instead of debiasing the continuation of a prompt, T5 debiases the input sentence itself, or more precisely, the text that is generated for the span in the input sentence that is replaced by a sentinel token. Is it also possible to use self-debiasing with T5 if there are more than one sentinel tokens in the input sentence? Moreover, I'm wondering if it is possible to debias an input sentence with T5 without having to first replace the biased words by sentinel tokens.

    opened by pullelys 0
Owner
Timo Schick
NLP Researcher @ SulzerGmbH , PhD Student @ CIS, LMU Munich
Timo Schick
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