The key insights from this content are:
Traditional topic modeling approaches like Latent Dirichlet Allocation (LDA) and BERTopic produce topics that are often too low-level and require significant interpretative work from analysts. These models focus on keyword co-occurrence and text similarity, rather than identifying high-level conceptual patterns.
The authors introduce "concept induction" as a new task that aims to extract high-level concepts from unstructured text, where each concept is defined by an explicit natural language description and inclusion criteria. This allows for more nuanced and theory-driven data analysis compared to generic topic labels.
The LLooM algorithm leverages large language models (LLMs) like GPT-3.5 and GPT-4 to iteratively synthesize concepts from text examples. It includes auxiliary operators to handle large datasets and produce nuanced concepts, rather than broad, generic ones.
LLooM is instantiated in the LLooM Workbench, a mixed-initiative text analysis tool that allows analysts to visualize, interact with, and refine the extracted concepts.
Evaluation across four real-world analysis scenarios shows that LLooM outperforms a state-of-the-art BERTopic model in terms of concept quality and data coverage. Expert case studies further demonstrate how LLooM can uncover novel insights even on familiar datasets.
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by Michelle S. ... at arxiv.org 04-19-2024
https://arxiv.org/pdf/2404.12259.pdfDeeper Inquiries