Cleaning Up Survey Responses with OpenAI’s GPT: A Comprehensive Guide

In the fast-paced world of data science and artificial intelligence, the challenge of cleaning and organizing vast amounts of data is a familiar hurdle. This task, often seen as mundane and time-consuming, has found a new, innovative solution through the use of Large Language Models (LLMs) like OpenAI’s GPT. Today, we delve into a compelling case study: the cleansing of survey responses for Study Fetch, an AI-powered platform dedicated to enhancing student learning experiences.

Study Fetch, confronted with over 10,000 survey responses from university students, faced a significant challenge. The “major” field in their survey was a free-form text box, leading to a wide variety of responses, from “Anthropology” and “Chem E” to abbreviations like “cs” for computer science. The diversity and informality of these responses posed a daunting task for data analysis.

Enter the power of OpenAI’s GPT model, specifically the gpt-3.5-turbo, which has been trained on a vast corpus of text data and fine-tuned with techniques such as reinforcement learning from human feedback (RLHF). This model, with its 175 billion+ parameters, offers an efficient solution for classifying these diverse responses into standardized categories.

The process began with the development of a precise prompt that instructed the LLM to categorize each survey response into predefined categories such as Arts and Humanities, Social Sciences, and Engineering and Technology. The initial prompt, designed for individual record processing, underwent several iterations to optimize cost and efficiency. The final version requested a JSON output format for easier parsing, demonstrating the importance of prompt engineering in leveraging LLMs effectively.

The technical implementation involved Node.js, a choice driven by the client’s preference. The script read the survey responses from a CSV file, constructed the prompt for the LLM, and parsed the JSON output to map each response to its corresponding category. This automation significantly reduced the manual effort required for data cleansing.

The GitHub repository hosting the full code can be found [here](https://github.com/aaxis-nram/data-cleanser-llm-node), providing a valuable resource for developers facing similar data standardization challenges.

This case study not only showcases the practical application of LLMs in data cleansing but also highlights the broader potential of these models in various data processing tasks. From deduplication and summarization to sentiment analysis, LLMs offer powerful tools for enhancing data quality and extracting meaningful insights.

However, it’s crucial to approach these tools with caution. Despite their capabilities, LLMs are not infallible and can sometimes produce errors or “hallucinations.” Therefore, combining their strengths with human oversight and domain expertise ensures the integrity and reliability of the data cleansing process.

In conclusion, the successful application of OpenAI’s GPT model in cleaning up survey responses for Study Fetch illustrates the transformative potential of LLMs in handling complex data challenges. As we continue to explore and refine these technologies, the horizon of possibilities for data science and AI applications expands, promising more efficient, accurate, and insightful outcomes across industries.