EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910557611753472 |
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| author | Feng, Zhaopeng Meng, Zijie Liu, Zuozhu |
| author_facet | Feng, Zhaopeng Meng, Zijie Liu, Zuozhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02970 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization Feng, Zhaopeng Meng, Zijie Liu, Zuozhu Computation and Language 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. |
| title | EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.02970 |