arXiv:2602.17907v2 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity. In this work, we introduce a novel topic model training framework by Distilling Soft Labels (DSL) from Language Models (LMs). To construct the contextually enriched reconstruction signals, we project the next token probabilities, conditioned on a specialized prompt, onto a pre-defined vocabulary, and train the topic models to reconstruct the sof
This work is published as large language models (LLMs) continue to demonstrate superior contextual understanding, addressing long-standing limitations in traditional topic modeling like data sparsity and lack of context.
Improved topic modeling techniques enhance the ability to extract meaningful insights from vast unstructured text data, critical for intelligence, research, and automated content analysis.
Topic models can now leverage the rich contextual embeddings of large language models, leading to more accurate, nuanced, and robust content classification and understanding.
- · AI researchers
- · Data analysis platforms
- · Content aggregators
- · NLP developers
- · Traditional BoW topic modeling methods
- · Systems relying on naive text analysis
- · Organizations slow to adopt advanced NLP
More sophisticated and accurate categorization of text data becomes possible across various applications.
This could lead to more effective information retrieval, trend detection, and automated knowledge graph construction.
Improved topic modeling might accelerate the development of more intelligent and context-aware AI agents capable of deeper understanding and interaction.
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Read at arXiv cs.AI