LITA uses seed-initialized clustering and selectively asks an LLM to reclassify only ambiguous documents, reducing API calls by over 80 percent while matching or beating full-prompt baselines.
Transactions of the Association for Computational Linguistics5, 529–542 (2017) 12 Chang et al
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LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework
LITA uses seed-initialized clustering and selectively asks an LLM to reclassify only ambiguous documents, reducing API calls by over 80 percent while matching or beating full-prompt baselines.