LLaSA: Large Language and E-Commerce Shopping Assistant

TL;DR

This paper introduces LLaSA, a large language model-based e-commerce shopping assistant trained on a diverse dataset, demonstrating strong performance and generalization across multiple tasks and winning top rankings in a major challenge.

Contribution

The paper presents LLaSA, a novel omnipotent shopping assistant built with instruction tuning on a large, diverse dataset, addressing task-specificity and generalization issues of prior models.

Findings

LLaSA achieved 3rd place in the Amazon KDD Cup 2024 Challenge.

LLaSA secured top-5 rankings across all challenge tracks.

Instruction tuning on EshopInstruct enhances multi-task performance.

Abstract

The e-commerce platform has evolved rapidly due to its widespread popularity and convenience. Developing an e-commerce shopping assistant for customers is crucial to aiding them in quickly finding desired products and recommending precisely what they need. However, most previous shopping assistants face two main problems: (1) task-specificity, which necessitates the development of different models for various tasks, thereby increasing development costs and limiting effectiveness; and (2) poor generalization, where the trained model performs inadequately on up-to-date products. To resolve these issues, we employ Large Language Models (LLMs) to construct an omnipotent assistant, leveraging their adeptness at handling multiple tasks and their superior generalization capability. Nonetheless, LLMs lack inherent knowledge of e-commerce concepts. To address this, we create an instruction dataset comprising 65,000 samples and diverse tasks, termed as EshopInstruct. Through instruction tuning on our dataset, the assistant, named LLaSA, demonstrates the potential to function as an omnipotent assistant. Additionally, we propose various inference optimization strategies to enhance performance with limited inference resources. In the Amazon KDD Cup 2024 Challenge, our proposed method, LLaSA, achieved an overall ranking of 3rd place on ShopBench, including 57 tasks and approximately 20,000 questions, and we secured top-5 rankings in each track, especially in track4, where we achieved the best performance result among all student teams. Our extensive practices fully demonstrate that LLMs possess the great potential to be competent e-commerce shopping assistants.