APOLO: Automatic Prompt Optimization for Ontology Learning
This work casts OL as an explicit prompt optimization problem over LLM modules, and proposes two ontology learner architectures: a greedy and an autoregressive learner, and optimize both using GEPA, a greedy evolutionary prompt optimizer built on DSPy.
Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu et al.
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