Educating Language Models as Promoters: Multi-Aspect Instruction Alignment With Self-Augmentation

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Abstract
E-commerce content generation necessitates creating engaging and customer-centric material to endorse products and enhance user satisfaction. Existing methods depend on task-specific feature design, which requires a fine-tailored model for each task with complex data collection and pre-processing, and their generation capabilities are limited. Meanwhile, large language models have demonstrated their capabilities in diverse natural language processing tasks, solving multiple tasks in a unified process. To address the concerns in e-commerce content generation, we leverage the impressive generation performance of large language models and propose a framework to educate them as proficient promoters in various e-commerce-related tasks. Our framework involves two modules: self-educating proliferates task instructions and data by instructing the unaligned model, and multi-aspect instruction alignment educates the language model by embedding all e-commerce tasks in a unified framework. The proposed model, Promoter, can perform a batch of prediction and generation tasks, working as a smart and creative promoter that only requires a quick view of the customer profile. Extensive experiments from automatic and human perspectives indicate that Promoter achieves state-of-the-art performances in various generation tasks, bringing the productivity of large language models to e-commerce in an integrated pipeline.
Funding Information
  • Hong Kong Research Grants Council
  • General Research Fund (PolyU 15200021, PolyU 15200023)
  • Australian Research Council (DP220103717, LE220100078)

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