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DISCO: Distilling Phrasal Counterfactuals with Large Language Models

This is the public codebase for arxiv paper: DISCO: Distilling Counterfactuals with Large Language Models

About DISCO

DISCO is a framework for automatically generating high-quality counterfactual data at scale. The system elicite counterfactual perturbations by prompting a large general language model like GPT-3. Then, a task-specific teacher model filters the generation to distill high-quality counterfactual data.

We find that learning with this counterfactual data yields a comparatively small student model that is 6% (absolute) more robust and generalizes 5% better across distributions than baselines on various challenging evaluations. This model is also 15% more sensitive in differentiating original and counterfactual examples, on three evaluation sets written by human workers and via human-AI collaboration.

Features TODO

• Enable KNN-and-sentence-embedding-based demonstration search.
• Enable reinforcement learning for demonstration search.
• Add option to turn on & off database features.
• Add prompt supports for Single-Sentence-Classification (ex, sentiment analysis).
• Return top-5 log-likelihood from OpenAI API calls.

Install MongoDB

Following the instruction here to install MongoDB local server on your machine. Also install MongoDB Compass for user-friendly database management. Download the version propoer to your operating system.

Requirements

python==3.10.9
numpy==1.24.2
openai==0.27.0
torch==2.0.1
transformers==4.26.1
hydra-core==1.3.2
pymongo==4.3.3
sentence-transformers==2.2.2

Install all required python dependencies:

pip install -r requirements.txt

For any other packages that may be required, please see the error messages and install accordingly.

Run LLM Counterfactual Distillation

We use Hydra to dynamically manage the configurations for the distillation pipeline. First, take a look at the config.yaml file loacted under the config folder. Set the argumentes required for your running and experiment:

# paths:
  data_dir: ./data/                   # path to the data directory, default to ./data

# data_params:
  dataset: snli                       # base dataset for conterfactual generation
  task_name: snli                     # the type of task defined in prompt.retrieval
  start: 0                            # start position of the dataset for the current batch
  end: 11                             # end position of the dataset for the current batch
  template_name: masked_cad_premise   # template name for counterfactual generation 
  source_label: entailment            # source label for counterfactual generation
  target_label: neutral               # target label for counterfactual generation

# generator_params:
  gen_type: completion                # gpt-3 generation mode: completion | insertion | chat
  model_name: gpt-3-003               # engine name: gpt-3-003 | gpt-3-002 | chatgpt | gpt-4
  overgenerate: True                  # more than 1 generations for an input data
  no_demo: False                      # zero-shop generation without in-context examples

# search_params: 
  num_neighbors: 8                             # number of nearest neighbors
  embed_type: CLS                              # type of embedding method
  metric: euclidean                            # distance metric for nearest neighbor
  encoder_name: roberta-large-nli-mean-tokens  # encoder name for sentence embedding
  prompt_search: False                         # enable dynamic demonstration selection 

# filter_params:
  filter_all: Flase                    # filter all previous generation outputs

Next, to run the distillation pipeline, execute:

python run.py

To run filtering on all previous generated outputs, set filter_all to False in the config file and re-execute:

python run.py

Generated DISCO Data

• You can find the DISCO augmentation data used for our experiments here: data/disco.jsonl
• For a more extensive set of counterfactual data, you can take a look at the data/augment folder
• Feel free to generate and filter more counterfactual data using the pipeline.

License

This work is licensed under a MIT License.

Cite

If you find that our paper, code, or the dataset inspire you, please cite us:

@misc{chen2022disco,
      title={DISCO: Distilling Phrasal Counterfactuals with Large Language Models}, 
      author={Zeming Chen and Qiyue Gao and Kyle Richardson and Antoine Bosselut and Ashish Sabharwal},
      year={2022},
      eprint={2212.10534},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}