Beyond Topic Modeling: Comparative Evaluation of Topic Interpretation by Large Language Models

This study investigates the application of Large Language Models (LLMs) for interpreting topics derived from topic modeling within the domain of restaurant reviews in Brazilian Portuguese. Traditional topic modeling techniques often produce outputs that require further interpretation to fully capture the nuanced meanings within the data. This research leverages the advanced natural language processing capabilities of LLMs to provide deeper insights into these topics, aiming to bridge the gap between computational topic identification and human-like understanding. A comparative analysis of several LLMs, including ChatGPT versions 3.5 and 4.0 and Google’s BARD, was conducted to assess their efficacy in interpreting the generated topics from a large dataset of restaurant reviews. The topics identified were subjected to interpretation by both human evaluators and LLMs, enabling a direct comparison between human and machine-generated interpretations. Preliminary results indicate that LLMs, especially with well-crafted prompts, can produce interpretations that are closely aligned with human understanding, underscoring their potential utility in qualitative data analysis within NLP applications. This research not only sheds light on the interpretative capabilities of LLMs but also opens new pathways for automating complex interpretive tasks across diverse linguistic and cultural landscapes.