Goal-Driven Explainable Clustering via Language Descriptions

This repo contains the datasets and the Propose-Assign-Select (PAS) method implementation for the paper Goal-Driven Explainable Clustering via Language Descriptions.

Authors: Zihan Wang, Jingbo Shang, Ruiqi Zhong

In Goal-Driven Explainable (GoalEX) clustering, the input is a corpora and a goal to cluster the corpora with. The output is a set of clusters, each with a description that matches with the goal. When applied recursively, GoalEx can generate an entire taxonomy.

• Goal-Driven Explainable Clustering via Language Descriptions
• Setup
• Dataset
• PAS
  • Running on a debug dataset
  • Running on a clustering dataset
  • Saving budget
  • Creating a hierarchy
• Citation

Setup

We need to install pytorch. Please follow the instructions here.

Then, install the rest of the dependencies:

pip install -r requirements.txt

If you need to use openai api's, you will need to get an api key here. You will also need to export the api key and organization account in the environment variables:

export OPENAI_API_KEY=sk-xx
export OPENAI_ORG=org-xx

Dataset

All datasets in the paper are in processed_data. By default, they are a json file (data.json) that matches with the fields defined in src/cluster_problem.py, which is

• goal: A natural language goal, such as "I would like to cluster them based on topics; each cluster should have a description of 'has a topic of '"
• texts: A list of strings, each an indiviual text sample in the corpora
• example_descriptions: [Optional] a list of strings, for example, ['has a topic of healthcare', 'has a topic of education']

Additionally, we provide labels for these problems to evaluate on. The labels (labels.json) are in the same folder, with the format

• labels: A list of integers, each an integer label for the corresponding text sample in the corpora
• class_descriptions: A list of strings, each a description of the corresponding class

The format of processed_data/real_data is slightly different, please check src/recursive_cluster.py for loading it.

PAS

Here, we show how to use our method, Propose-Assign-Select (PAS), for solving this task. You can specify an exp_dir as an argument to it and the results will be saved in exp_dir/cluster_result.json.

In most cases we use a flan-t5 model as the assigner, so it is reccomended to have a GPU.

Running on a debug dataset

You will need to have set up the openai api key and organization account in the set up step. The GPU memory required for the flan-t5-xl assigner is around 20GB.
To test the code, we provide a debug dataset processed_data/debug. You can run the following command:

python src/iterative_cluster.py \
  --data_path processed_data/debug \
  --exp_dir experiments/debug \
  --proposer_model gpt-3.5-turbo \
  --assigner_name google/flan-t5-xl \
  --proposer_num_descriptions_to_propose 10 \
  --cluster_num_clusters 6 \
  --verbose

Running on a clustering dataset

Here is also a list of recommended proposers and assigners:

• Proposers: gpt-3.5-turbo, gpt-4
• Assigners: google/flan-t5-xl, gpt-3.5-turbo

We will estimate the query cost if using openai proposer and assigners, before starting the algorithm. For an example, let's test on the AG's News dataset.

python src/iterative_cluster.py \
  --data_path processed_data/agnews \
  --exp_dir experiments/agnews \
  --subsample 1024 \
  --proposer_model gpt-3.5-turbo \
  --assigner_name google/flan-t5-xl \
  --proposer_num_descriptions_to_propose 30 \
  --assigner_for_final_assignment_template templates/t5_multi_assigner_one_output.txt \
  --cluster_num_clusters 4 \
  --verbose

You can add --with_labels to automatically calculate the performance between the generated clusters and the ground truth labels.

Saving budget

1. We recommend using a subsample of the dataset to save budget.
2. Experiment with a FlanT5 assigner, and only use a GPT-3.5-turbo assigner if the proposed descriptions are good, but the found clusters are subpar.

Creating a hierarchy

We can generate a taxonomy of model errors for human feedbacks of model-generated summaries, as shown in the figure above.

Our code supports creating a hierarchy of clusters by recursively applying the PAS. By default, we use a gpt-3.5-turbo proposer and gpt-3.5-turbo assigner, and a search for a 2-level hierarchy, with 8 clusters at each node.
IMPORTANT We turn off asking for approval for the recursive clustering, so please be mindful of the cost when using this feature.

As an example with the datasets we expeirmented in the paper, run

python src/recursive_cluster.py

Citation

Please cite our paper if you find this repo useful:

@article{wang2023goal,
  title={Goal-Driven Explainable Clustering via Language Descriptions},
  author={Wang, Zihan and Shang, Jingbo and Zhong, Ruiqi},
  journal={arXiv preprint arXiv:2305.13749},
  year={2023}
}