Summary of Investigating Llm Applications in E-commerce, by Chester Palen-michel et al.
Investigating LLM Applications in E-Commerce
by Chester Palen-Michel, Ruixiang Wang, Yipeng Zhang, David Yu, Canran Xu, Zhe Wu
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The emergence of Large Language Models (LLMs) has revolutionized natural language processing in various applications, particularly in e-commerce. This paper explores the efficacy of LLMs in the e-commerce domain by instruction-tuning an open-source LLM model with public e-commerce datasets and comparing its performance to traditional pre-trained models. The study conducts a comprehensive comparison between LLMs and conventional models across tasks such as classification, generation, summarization, and named entity recognition (NER). Additionally, it examines the effectiveness of very large LLMs using in-context learning in e-commerce-specific tasks. The findings suggest that few-shot inference with very large LLMs often does not outperform fine-tuning smaller pre-trained models, highlighting the importance of task-specific model optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have changed how we process language, especially in e-commerce. This research looks at how well LLMs work for specific tasks like classifying products, generating product descriptions, and recognizing important information. They compared these new models to older ones used in industry and found that while big LLMs are great, they’re not always the best choice. Sometimes smaller models, fine-tuned for a specific task, perform better. |
Keywords
» Artificial intelligence » Classification » Few shot » Fine tuning » Inference » Instruction tuning » Named entity recognition » Natural language processing » Ner » Optimization » Summarization
