LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following
This work addresses e-commerce authoring challenges for customers, sellers, and platforms, but it is incremental as it adapts existing LLM methods to a specific domain.
The paper tackled the problem of limited domain-specific feature memorization in large language models for e-commerce authoring by proposing LLaMA-E, a unified model that achieved state-of-the-art performance and demonstrated advantages in zero-shot practical applications.
E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have revolutionized content generation, they often fall short in e-commerce applications due to their limited memorization of domain-specific features. This paper proposes LLaMA-E, the unified e-commerce authoring models that address the contextual preferences of customers, sellers, and platforms, the essential objects in e-commerce operation. We design the instruction set derived from tasks of ads generation, query-enhanced product title rewriting, product classification, purchase intent speculation, and general e-commerce Q&A. The instruction formulation ensures the interleaved cover of the presented and required object features, allowing the alignment of base models to parameterise e-commerce knowledge comprehensively. The proposed LLaMA-E models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. To our knowledge, this is the first LLM tailored to empower authoring applications with comprehensive scenario understanding by integrating features focused on participated objects.