EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization

• URL: http://arxiv.org/abs/2408.02970v1
• Date: Tue, 6 Aug 2024 05:50:41 GMT
• Title: EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization
• Authors: Zhaopeng Feng, Zijie Meng, Zuozhu Liu,
• Abstract summary: EC-Guide hrefhttps://github.com/fzp0424/EC-Guide-KDDUP-2024 is a comprehensive e-commerce guide for instruction tuning and quantization of LLMs. Our solution is model-agnostic, enabling effective scalability across larger systems.
• Score: 7.982538359035973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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