Making Large Language Models Better Knowledge Miners for Online
  Marketing with Progressive Prompting Augmentation
Chunjing Gan Dan Yang Binbin Hu Ziqi Liu Yue Shen Zhiqiang Zhang Jinjie Gu Jun Zhou Guannan Zhang
Nowadays, the rapid development of mobile economy has promoted the
flourishing of online marketing campaigns, whose success greatly hinges on the
efficient matching between user preferences and desired marketing campaigns
where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG)
could serve as the critical "bridge" for preference propagation. In this paper,
we seek to carefully prompt a Large Language Model (LLM) with domain-level
knowledge as a better marketing-oriented knowledge miner for marketing-oriented
knowledge graph construction, which is however non-trivial, suffering from
several inevitable issues in real-world marketing scenarios, i.e.,
uncontrollable relation generation of LLMs,insufficient prompting ability of a
single prompt, the unaffordable deployment cost of LLMs. To this end, we
propose PAIR, a novel Progressive prompting Augmented mIning fRamework for
harvesting marketing-oriented knowledge graph with LLMs. In particular, we
reduce the pure relation generation to an LLM based adaptive relation filtering
process through the knowledge-empowered prompting technique. Next, we steer
LLMs for entity expansion with progressive prompting augmentation,followed by a
reliable aggregation with comprehensive consideration of both self-consistency
and semantic relatedness. In terms of online serving, we specialize in a small
and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality
corpus provided by a strong teacher-LLM. Extensive experiments and practical
applications in audience targeting verify the effectiveness of the proposed
(Light)PAIR.