Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling
This work addresses the need for more accurate topic modeling in dynamic and complex text data, such as in talent management research, though it appears incremental as it builds on existing clustering-based methods.
The paper tackles the problem of topic modeling's limited ability to capture nuanced semantics in text corpora by integrating large language models (LLMs) with clustering-based approaches, resulting in improved topic coherence (70% vs. 65% and 57% benchmarks) and diversity (95.5% vs. 85% and 72% benchmarks) on news articles.
Topic modeling is a widely used technique for uncovering thematic structures from large text corpora. However, most topic modeling approaches e.g. Latent Dirichlet Allocation (LDA) struggle to capture nuanced semantics and contextual understanding required to accurately model complex narratives. Recent advancements in this area include methods like BERTopic, which have demonstrated significantly improved topic coherence and thus established a new standard for benchmarking. In this paper, we present a novel approach, the Qualitative Insights Tool (QualIT) that integrates large language models (LLMs) with existing clustering-based topic modeling approaches. Our method leverages the deep contextual understanding and powerful language generation capabilities of LLMs to enrich the topic modeling process using clustering. We evaluate our approach on a large corpus of news articles and demonstrate substantial improvements in topic coherence and topic diversity compared to baseline topic modeling techniques. On the 20 ground-truth topics, our method shows 70% topic coherence (vs 65% & 57% benchmarks) and 95.5% topic diversity (vs 85% & 72% benchmarks). Our findings suggest that the integration of LLMs can unlock new opportunities for topic modeling of dynamic and complex text data, as is common in talent management research contexts.