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.