Summary of Ecellm: Generalizing Large Language Models For E-commerce From Large-scale, High-quality Instruction Data, by Bo Peng et al.
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
by Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenges of developing effective e-commerce models that can generalize well to new users and products. Conventional approaches have shown limited success in this domain, while large language models (LLMs) have demonstrated outstanding performance in generalist modeling and out-of-domain generalizability. To fully leverage the power of LLMs for e-commerce, the authors construct ECInstruct, a benchmark instruction dataset for e-commerce, and develop eCeLLM, a series of e-commerce LLMs by instruction-tuning general-purpose LLMs. The results show that eCeLLM models substantially outperform baseline models, including GPT-4 and state-of-the-art task-specific models, in both in-domain and out-of-domain evaluations. This highlights the potential of eCeLLM as a generalist e-commerce model. The ECInstruct dataset and eCeLLM models are publicly available for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make online shopping more efficient by creating better models that can understand new products and customers. Right now, current models don’t do very well with new situations. Large language models have shown they’re good at understanding many types of text, but not specifically for e-commerce. To fix this, the authors create a special dataset called ECInstruct and train new models using this data. They found that these new models can understand text about new products and customers much better than before. This is important because it means we can use these models to make online shopping more personalized and efficient. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning
