LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following

Kaize Shi, Xueyao Sun, Dingxian Wang, Yinlin Fu, Guandong Xu, Qing Li


Abstract
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 parameterize 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.
Anthology ID:
2025.coling-main.58
Volume:
Proceedings of the 31st International Conference on Computational Linguistics
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
870–885
Language:
URL:
https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.58/
DOI:
Bibkey:
Cite (ACL):
Kaize Shi, Xueyao Sun, Dingxian Wang, Yinlin Fu, Guandong Xu, and Qing Li. 2025. LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following. In Proceedings of the 31st International Conference on Computational Linguistics, pages 870–885, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (Shi et al., COLING 2025)
Copy Citation:
PDF:
https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.58.pdf