How to train ChatGPT on custom data efficiently?

Question

I am working with a dataset (csv format) and creating a custom trained chatbot using the ChatGPT API in Python. Approximately there are 1000 observations and 12 variables. I was able to train the model, however when using asking questions, the chatbot does not give the required results. For example when I ask "What is the average age of the employees?" the result that I get is 15.5, which is incorrect (should be around 40). An other example, "How many males are there in the dataset?", the output is 60, however there are 340 males in the dataset.

Answer 1

I'm sorry to hear that you're facing issues with your custom trained chatbot. The problem you described seems to be related to the model's understanding of the dataset and the questions being asked. There could be several reasons for the incorrect responses:

Insufficient training data: With 1000 observations, the dataset might be relatively small for training a language model like ChatGPT. The model might not have seen enough examples to accurately answer complex questions.

Data format and preprocessing: Check if the dataset is properly formatted and preprocessed before training. Data cleaning and formatting are crucial to ensure the model can understand and learn from the data effectively.

Ambiguity in questions: If the questions are not phrased precisely or contain ambiguous terms, the model may struggle to provide accurate answers.

Model hyperparameters: The performance of the model can be influenced by hyperparameters chosen during training. Adjusting these parameters might lead to better results.