Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?
This evaluation shows that while LLMs effectively uncover a substantial portion of implicitly-related touchpoints, significant room for improvement remains in their selection performance, and offers a new roadmap for transitioning conversion attribution from mechanical rule-matching to human-aligned semantic reasoning.
Jinqi Wu, Sishuo Chen, Zhangming Chan et al.
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