LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability

Researchers propose a novel framework treating Large Language Models as attention-informed Neural Topic Models, enabling interpretable topic extraction from documents. The approach combines white-box interpretability analysis with black-box long-context LLM capabilities, demonstrating competitive performance on topic modeling tasks while maintaining semantic clarity.

This research bridges two previously distinct domains—neural topic modeling and large language models—by demonstrating that LLMs contain latent structures analogous to classical topic models. The white-box analysis reveals how attention mechanisms in LLMs naturally encode document-topic and topic-word distributions, effectively functioning as sophisticated topic models without explicit architectural modifications. This finding validates theoretical assumptions about LLM representational capacity.

The work addresses a longstanding limitation in both fields: classical neural topic models struggle with semantic abstraction and representation constraints, while black-box LLMs lack interpretability. By reformulating topic modeling as a structured long-input generation task, the researchers leverage LLM strengths while maintaining output interpretability through post-generation signal compensation using diversified topic cues and hybrid retrieval mechanisms.

For practitioners and researchers, this framework enables more reliable content analysis at scale. Organizations can deploy existing LLM infrastructure for topic extraction without additional model training, while gaining interpretable, auditable results. The demonstrated effectiveness of long-context LLMs suggests that document understanding tasks previously requiring specialized models now benefit from general-purpose LLM capabilities.

The connection between LLMs and NTMs has implications for foundation model development and fine-tuning strategies. Understanding how attention naturally implements topic structure informs architectural decisions for improving model interpretability without sacrificing performance. The success of this hybrid approach indicates that future AI systems may benefit from explicitly leveraging these discovered structural patterns.

• →LLMs naturally encode topic modeling structures in their attention mechanisms, functioning as implicit attention-informed neural topic models.
• →White-box analysis of LLM attention reveals interpretable document-topic and topic-word distributions matching classical neural topic model outputs.
• →Black-box long-context LLMs achieve competitive or superior topic modeling performance through structured generation with post-hoc signal compensation.
• →The framework enables practical topic extraction using existing LLM deployments without requiring specialized model training or architecture modifications.
• →These findings establish a theoretical connection between LLMs and classical topic modeling, informing future advances in interpretable AI systems.