🛒 Amazon KDD CUP 2024: Multi-Task Online Shopping Challenge for LLMs Starter Kit
This repository is the Amazon KDD Cup 2024 Submission template and Starter kit! Clone the repository to compete now!
This repository contains:
- Documentation on how to submit your models to the leaderboard
- The procedure for best practices and information on how we evaluate your model, etc.
- Starter code for you to get started!
Table of Contents
- Competition Overview
- Dataset
- Tasks
- Evaluation Metrics
- Getting Started
- Frequently Asked Questions
- Important Links
📖 Competition Overview
Online shopping is complex, involving various tasks from browsing to purchasing, all requiring insights into customer behavior and intentions. This necessitates multi-task learning models that can leverage shared knowledge across tasks. Yet, many current models are task-specific, increasing development costs and limiting effectiveness. Large language models (LLMs) have the potential to change this by handling multiple tasks through a single model with minor prompt adjustments. Furthermore, LLMs can also improve customer experiences by providing interactive and timely recommendations. However, online shopping, as a highly specified domain, features a wide range of domain-specific concepts (e.g. brands, product lines) and knowledge (e.g. which brand produces which products), making it challenging to adapt existing powerful LLMs from general domains to online shopping.
Motivated by the potentials and challenges of LLMs, we present ShopBench, a massive challenge for online shopping, with 57 tasks and ~20000 questions, derived from real-world Amazon shopping data. All questions in this challenge are re-formulated to a unified text-to-text generation format to accommodate the exploration of LLM-based solutions. ShopBench focuses on four main key shopping skills (which will serve as Tracks 1-4):