Reddit Content Moderation with LLMs

Exploring the application of Large Language Models for efficient content moderation on Reddit

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About the Project

Reddit Content Moderation with LLMs is a research-driven project based on the paper "Watch Your Language: Investigating Content Moderation with Large Language Models". This project focuses on the first phase of the study, which evaluates the rule-based moderation capabilities of Large Language Models (LLMs).

The primary objective of the project is to optimize the content moderation pipeline by significantly reducing operational costs through two key improvements:

• GPT-4o Mini: Leveraging OpenAI's cost-efficient model instead of GPT-3.5 Turbo. This model dramatically lowers the cost of processing a million input tokens from $3.00 to $0.15 and output tokens from $6.00 to $0.60.
• Batch API: Utilizing OpenAI's Batch API to further reduce token processing costs by 50%. This approach requires batch submission of requests, with responses provided within 24 hours.

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Getting Started

Prerequisites

To use this project, ensure you have the following:

• OpenAI API Key: Required for making API calls.
• OpenAI Account with Credit Balance (Optional): Enables higher API usage within shorter timeframes.

Installation

1. Clone the repository:

   git clone https://github.com/NicolasArroyo/llm-reddit-moderation.git

2. Navigate to the project directory:

   cd llm-reddit-moderation

3. Install the required dependencies:

   pip install -r requirements.txt

4. Create a .env file with the following key:

   OPEN_AI_KEY="YOUR API KEY"
   

5. Download the curated data from the official repository of the research paper and place it in the project folder.

6. Ensure your project directory is structured as follows:

   llm-reddit-moderation/
   ├── batch_pipeline
   ├── markdown_utils.py
   ├── preprocess_data_pipeline.py
   ├── requirements.txt
   └── data/
       ├── subreddit_rules_w_description.jsonl
       └── subreddit_balanced_datasets/
   

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Usage

To run the project for the first time do the following:

1. Run the preprocessing script to create .jsonl files for each subreddit that will be sent through the Batch API:

   python3 preprocess_data_pipeline.py

2. Ensure the function call to submit_all_subreddits in batch_pipeline.py is uncommented. This step is required to upload the preprocessed data to OpenAI servers before performing the content moderation task for the first time.

3. Set the subreddit in batch_pipeline.py by modifying the subreddit variable in the main function to the subreddit you want to analyze:

   subreddit = "askscience"

4. Execute batch_pipeline.py to begin processing. Monitor the terminal for real-time updates on the status of your batch requests as they are processed by OpenAI:

   python3 batch_pipeline.py

5. View the results: Once processing is complete, the terminal will display metrics summarizing the content moderation analysis for the selected subreddit:

   {'accuracy': 0.75, 'precision': 0.682, 'recall': 0.936, 'f1': 0.789}
   

All Batch API results will be stored in the batch_results directory, organized by subreddit:

llm-reddit-moderation/
    └── batch_results/
        ├── askscience.jsonl
        └── worldnews.json

Additionally, an index file named subreddit_batch_file_index.jsonl will be maintained to track the following details:

• file_id: The unique identifier for the preprocessed data file associated with each subreddit.
• batch_id: The unique identifier for each batch submission via batch_pipeline.py for a specific subreddit.
• response_file_id: The unique identifier for the response file generated for each subreddit batch submission.

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Findings

Our analysis builds on the cost and time comparisons derived from Table 10 of the referenced research paper:

Model	Cost ($)	Time Taken (days)
GPT-3.5	175	5

Scope of Analysis

Due to the financial constraints of running the entire pipeline across all 95 subreddits, we selected a subset of subreddits for analysis. These include:

• r/askscience
• r/IAmA
• r/movies
• r/worldnews
• r/depression (included due to its significant size, despite not being mentioned in the paper)

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Cost Analysis

The pipeline was executed for the selected subreddits, producing the following cost breakdown:

Subreddit	Input Tokens Cost ($)	Output Tokens Cost ($)	Total Cost ($)
askscience	0.0351	0.0177	0.0529
IAmA	0.0251	0.0158	0.0409
movies	0.0648	0.0177	0.0825
worldnews	0.0283	0.0183	0.0466
depression	0.0864	0.0237	0.1102

Cost Projection for All Subreddits

Using r/depression as the worst-case scenario (highest cost), we estimate the total cost to process all 95 subreddits at approximately $10.47, a substantial reduction from the original $175 reported for GPT-3.5.

Note: This is a conservative estimate. A full pipeline run across all subreddits is necessary to obtain precise cost figures.

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Time Analysis

According to OpenAI's Batch API documentation, all requests are guaranteed to complete within a maximum time frame of 24 hours. This represents a significant improvement over the previously reported 5-day processing time for GPT-3.5.

In practice, batch completion times might be faster than this worst-case scenario. Our experimental results for the selected subreddits are summarized below:

Subreddit	Time Taken (minutes)
askscience	21
IAmA	16
movies	20
worldnews	39
depression	17

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Acknowledgments

This project utilizes curated data, a modified prompt, and an adapted response format derived from the research paper Watch Your Language: Investigating Content Moderation with Large Language Models, specifically its official repository.

Special thanks to Deepak Kumar, Yousef Anees AbuHashem, and Zakir Durumeric for their work, which inspired further exploration into cost-effective approaches to content moderation.