DSL-Topic: Improving Topic Modeling by Distilling Soft Labelsfrom Language Models

Researchers introduce DSL-Topic, a novel framework that improves neural topic modeling by distilling soft labels from language models rather than relying on traditional bag-of-words reconstruction. The approach leverages LM-generated contextual signals to produce higher-quality topics with better coherence and semantic alignment, demonstrating significant improvements over existing baselines.

DSL-Topic addresses fundamental limitations in traditional neural topic modeling by shifting from sparse bag-of-words optimization to contextually enriched learning signals. The framework extracts soft labels from language models using specialized prompts, enabling topic models to learn from richer semantic representations than document word frequencies alone. This methodological advancement reflects a broader trend in NLP where large language models increasingly serve as knowledge sources for training downstream tasks.

The technical innovation centers on leveraging LM hidden states and next-token probabilities as training signals. By conditioning token predictions on custom prompts, the framework creates contextually appropriate reconstruction targets that better capture thematic structure. This approach implicitly transfers knowledge from pre-trained LMs to topic models, reducing the impact of data sparsity—a persistent challenge in traditional topic modeling where rare word combinations and limited training data degrade performance.

For practitioners and researchers, this work enables more effective document organization and semantic retrieval without requiring task-specific labeled data. The demonstrated improvements in topic coherence directly translate to better information discovery systems, content recommendation engines, and document clustering applications. The retrieval-oriented evaluation metric shows particular promise for enterprise search and knowledge management systems where semantic relevance matters more than keyword matching.

The broader implications position distillation from foundation models as a viable strategy for enhancing classical NLP methods. As language models become increasingly accessible, similar distillation approaches could improve other traditionally supervised or unsupervised learning tasks, creating a new paradigm where foundation model knowledge systematically elevates legacy techniques.

• →DSL-Topic distills contextual soft labels from language models to train superior topic models beyond traditional bag-of-words approaches
• →The framework demonstrates substantial improvements in topic coherence and document assignment accuracy compared to existing baselines
• →Leveraging LM hidden states as training signals effectively reduces data sparsity challenges in neural topic modeling
• →Retrieval-based evaluation metrics confirm the approach significantly outperforms competitors for semantic document similarity tasks
• →The work exemplifies how foundation models can enhance classical NLP methods through knowledge distillation