This repository contains scripts and prompts for our paper "TopicGPT: Topic Modeling by Prompting Large Language Models".

• [11/18/23] Second-level topic generation code and refinement code are uploaded.
• [11/11/23] Basic pipeline is uploaded. Refinement and second-level topic generation code are coming soon.

• Install the requirements: pip install -r requirements.txt
• Set your OpenAI key according to this article.
• Refer to https://openai.com/pricing/ for OpenAI API pricing or to https://blog.perplexity.ai/blog/introducing-pplx-api for Perplexity API pricing.

• Prepare your .jsonl data file in the following format:

  { "id": "Optional IDs", "text": "Documents", "label": "Optional ground-truth labels" } 

• Put the data file in data/input. There is also a sample data file data/input/sample.jsonl to debug the code.
• If you want to sample a subset of the data for topic generation, run python script/data.py --data <data_file> --num_samples 1000 --output <output_file>. This will sample 1000 documents from the data file and save it to <output_file>. You can also specify --num_samples to sample a different number of documents, see the paper for more detail.
• Raw dataset: [link].

• You can either run script/run.sh to run the entire pipeline or run each step individually. See the notebook in script/example.ipynb for a step-by-step guide.

• Topic generation: Modify the prompts according to the templates in templates/generation_1.txt and templates/seed_1.md. Then, to run topic generation, do:

  python3 script/generation_1.py --deployment_name gpt-4 \ --max_tokens 300 --temperature 0.0 --top_p 0.0 \ --data data/input/sample.jsonl \ --prompt_file prompt/generation_1.txt \ --seed_file prompt/seed_1.md \ --out_file data/output/generation_1.jsonl \ --topic_file data/output/generation_1.md \ --verbose True 

• Topic refinement: If you want to refine the topics, modify the prompts according to the templates in templates/refinement.txt. Then, to run topic refinement, do:

  python3 refinement.py --deployment_name gpt-4 \ --max_tokens 500 --temperature 0.0 --top_p 0.0 \ --prompt_file prompt/refinement.txt \ --generation_file data/output/generation_1.jsonl \ --topic_file data/output/generation_1.md \ --out_file data/output/refinement.md \ --verbose True \ --updated_file data/output/refinement.jsonl \ --mapping_file data/output/refinement_mapping.txt \ --refined_again False \ --remove False 

• Topic assignment: Modify the prompts according to the templates in templates/assignment.txt. Then, to run topic assignment, do:

  python3 script/assignment.py --deployment_name gpt-3.5-turbo \ --max_tokens 300 --temperature 0.0 --top_p 0.0 \ --data data/input/sample.jsonl \ --prompt_file prompt/assignment.txt \ --topic_file data/output/generation_1.md \ --out_file data/output/assignment.jsonl \ --verbose True 

• Topic correction: If the assignment contains errors or hallucinated topics, modify the prompts according to the templates in templates/correction.txt (note that this prompt is very similar to the assignment prompt, only adding a {Message} field towards the end of the prompt). Then, to run topic correction, do:

  python3 script/correction.py --deployment_name gpt-3.5-turbo \ --max_tokens 300 --temperature 0.0 --top_p 0.0 \ --data data/output/assignment.jsonl \ --prompt_file prompt/correction.txt \ --topic_file data/output/generation_1.md \ --out_file data/output/assignment_corrected.jsonl \ --verbose True 

• Second-level topic generation: If you want to generate second-level topics, modify the prompts according to the templates in templates/generation_2.txt. Then, to run second-level topic generation, do:

  python3 script/generation_2.py --deployment_name gpt-4 \ --max_tokens 300 --temperature 0.0 --top_p 0.0 \ --data data/output/generation_1.jsonl \ --seed_file data/output/generation_1.md \ --prompt_file prompt/generation_2.txt \ --out_file data/output/generation_2.jsonl \ --topic_file data/output/generation_2.md \ --verbose True 

@misc{pham2023topicgpt, title={TopicGPT: A Prompt-based Topic Modeling Framework}, author={Chau Minh Pham and Alexander Hoyle and Simeng Sun and Mohit Iyyer}, year={2023}, eprint={2311.01449}, archivePrefix={arXiv}, primaryClass={cs.CL} }