Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data
Xinyi Ling, Bo Peng, Hanwen Du, Zhihui Zhu, Xia Ning·October 22, 2024
Summary
The paper introduces MMECInstruct, a large-scale multimodal instruction dataset for e-commerce, and CASLIE, a framework for integrating multimodal information. CASLIE models, fine-tuned using MMECInstruct, outperform advanced baseline models in in-domain evaluation and show strong generalizability to out-of-domain settings. This addresses challenges in leveraging multimodal e-commerce data for foundation models, including scarcity of high-quality datasets and lack of effective multimodal integration methods.
Background
Multimodal e-commerce data
Importance of multimodal data in e-commerce
Challenges in leveraging multimodal e-commerce data
Scarcity of high-quality datasets
Lack of effective multimodal integration methods
Objective
Contribution of the paper
Introduction of MMECInstruct dataset
Presentation of CASLIE framework
Goals
Addressing challenges in multimodal e-commerce data utilization
Improving performance of foundation models in e-commerce tasks
MMECInstruct: A Large-Scale Multimodal Instruction Dataset for E-commerce
Dataset Overview
Characteristics of MMECInstruct
Size and scope
Types of multimodal data included
Data collection process
Methods used for gathering data
Quality assurance
Techniques for ensuring data quality
CASLIE: A Framework for Integrating Multimodal Information in E-commerce
Framework Components
Core principles of CASLIE
Overview of the framework's design philosophy
Integration methods
Techniques for combining different modalities
Model architecture
Description of the models used in CASLIE
Evaluation and Performance
In-Domain Evaluation
Metrics used
Performance indicators
Results and analysis
Comparison with advanced baseline models
Out-of-Domain Generalizability
Evaluation setup
Description of the out-of-domain scenarios
Results and discussion
Performance in unseen domains
Conclusion
Summary of contributions
Recap of MMECInstruct and CASLIE
Impact on e-commerce and foundation models
Future work
Potential extensions of MMECInstruct and CASLIE
Areas for further research
Basic info
papers
computation and language
information retrieval
artificial intelligence
Advanced features