EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization
<|reference_start|>EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization: Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instruction tuning and quantization. E-commerce, with its complex tasks and extensive product-user interactions, presents a promising application area for LLMs. However, the domain-specific concepts and knowledge inherent in e-commerce pose significant challenges for adapting general LLMs. To address this issue, we developed EC-Guide \href{https://github.com/fzp0424/EC-Guide-KDDUP-2024}, a comprehensive e-commerce guide for instruction tuning and quantization of LLMs. We also heuristically integrated Chain-of-Thought (CoT) during inference to enhance arithmetic performance. Our approach achieved the 2nd place in Track 2 and 5th place in Track 5 at the Amazon KDD Cup'24 \href{https://www.aicrowd.com/challenges/amazon-kdd-cup-2024-multi-task-online-shopping-challenge-for-llms}. Additionally, our solution is model-agnostic, enabling effective scalability across larger systems.<|reference_end|>
@article{feng2024ec-guide:,
    title={EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and
  Quantization},
    author={Zhaopeng Feng, Zijie Meng, Zuozhu Liu},
    journal={arXiv preprint arXiv:2408.02970},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02970},
    primaryClass={cs.CL}
}