GPT Referee: Using GPT-4 to Evaluate Synthetically Generated Responses in Conversational Systems

Introduction:

At Bitext, we value data-driven analysis. Therefore, we’ve thoroughly assessed our Hybrid Datasets using our top-notch AI text generator. We initiated this assessment using GPT-4, which is well-regarded for evaluating language model responses. We examined our model’s outputs based on their relevance, clarity, accuracy, and completeness.

Methodology:

The assessment aimed at comparing our Hybrid Dataset’s performance against GPT-3.5 and GPT-4 based on four key aspects: relevance, clarity, accuracy, and completeness.

Evaluation Scores Comparison Results:

Our Hybrid Dataset outperformed GPT-3.5 by 20% and GPT-4 by 12%, scoring 105.

Real-world Application Analysis:

We also explored how our AI generator performs in real-world scenarios, as shown below:

For instance, our model provided a clear step-by-step guide for a “Cancel Order” query, scoring a 10. It offered a helpful response for “Registration Problems” query, scoring 8.

Conclusion:

In the assessment, it’s clear that better volume and quality of data yield better results. Our AI text generator is part of a process for making mixed datasets. We constantly work to improve data quality, which is used for both initial setup and fine-tuning. Our goal is to improve the evaluation scores of each dataset, providing businesses with specialized data for their conversational AI needs.

References