LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction
  Following
Kaize Shi Xueyao Sun Dingxian Wang Yinlin Fu Guandong Xu Qing Li
E-commerce authoring involves creating attractive, abundant, and targeted
promotional content to drive product sales. The emergence of large language
models (LLMs) introduces an innovative paradigm, offering a unified solution to
address various authoring tasks within this scenario. However, mainstream LLMs
trained on general corpora with common sense knowledge reveal limitations in
fitting complex and personalized features unique to e-commerce products and
customers. Furthermore, LLMs like GPT-3.5 necessitate remote accessibility,
raising concerns about safeguarding voluminous customer privacy data during
transmission. This paper proposes the LLaMA-E, the unified and customized
instruction-following language models focusing on diverse e-commerce authoring
tasks. Specifically, the domain experts create the seed instruction set from
the tasks of ads generation, query-enhanced product title rewriting, product
classification, purchase intent speculation, and general Q&A. These tasks
enable the models to comprehensively understand precise e-commerce authoring
knowledge by interleaving features covering typical service aspects of
customers, sellers, and platforms. The GPT-3.5 is introduced as a teacher
model, which expands the seed instructions to form a training set for the
LLaMA-E models with various scales. The experimental results show that the
proposed LLaMA-E models achieve state-of-the-art results in quantitative and
qualitative evaluations, also exhibiting the advantage in zero-shot scenes. To
the best of our knowledge, this study is the first to serve the LLMs to
specific e-commerce authoring scenarios.