Using an ML-predicted label count to constrain LLM generation and post-editing outputs to the LCSH vocabulary lifts subject-heading prediction F1 from 0.135 to 0.300 on a 2,100-book test set.
Automated knowledge organisation: Ai/ml-based subject indexing system for libraries
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A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models
Using an ML-predicted label count to constrain LLM generation and post-editing outputs to the LCSH vocabulary lifts subject-heading prediction F1 from 0.135 to 0.300 on a 2,100-book test set.