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arXiv 2408.12779cs.CLcs.AI
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探究 LLM 在电子商务中的应用

Investigating LLM Applications in E-Commerce

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Chester Palen-Michel, Ruixiang Wang, Yipeng Zhang, David Yu, Canran Xu, Zhe Wu

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AI总结：

本文探究了大型语言模型在电子商务领域的应用，通过指令微调开源模型并与传统预训练模型在分类、生成等任务上对比，发现超大型LLM的少样本推理通常不如微调较小模型，强调了任务特定优化的重要性。

摘要：

大型语言模型（LLMs）的出现彻底改变了自然语言处理在各个应用领域尤其是电子商务中的面貌。在此类领域应用 LLMs 之前，一个关键步骤是理解并比较其在不同任务用例中的性能。本文探讨了 LLMs 在电子商务领域的效能，重点在于使用不同规模的公开电子商务数据集对开源 LLM 模型进行指令微调，并将其性能与工业应用中普遍采用的传统模型进行比较。我们在电子商务领域特有的具体任务上，即分类、生成、摘要和命名实体识别（NER），对 LLMs 和传统预训练语言模型进行了全面比较。此外，我们还考察了当前超大型 LLM 在电子商务特定任务中，利用上下文学习的特定工业应用的有效性。研究结果表明，使用超大型 LLMs 的少样本推理通常不如微调较小的预训练模型，这凸显了任务特定模型优化的重要性。此外，我们还研究了不同的训练方法，如单任务训练、混合任务训练以及 LoRA 合并（包括领域/任务内和不同任务之间）。通过严谨的实验与分析，本文为 LLMs 提升电子商务行业自然语言处理能力的潜在有效性提供了有价值的见解。

英文摘要：

The emergence of Large Language Models (LLMs) has revolutionized natural language processing in various applications especially in e-commerce. One crucial step before the application of such LLMs in these fields is to understand and compare the performance in different use cases in such tasks. This paper explored the efficacy of LLMs in the e-commerce domain, focusing on instruction-tuning an open source LLM model with public e-commerce datasets of varying sizes and comparing the performance with the conventional models prevalent in industrial applications. We conducted a comprehensive comparison between LLMs and traditional pre-trained language models across specific tasks intrinsic to the e-commerce domain, namely classification, generation, summarization, and named entity recognition (NER). Furthermore, we examined the effectiveness of the current niche industrial application of very large LLM, using in-context learning, in e-commerce specific tasks. Our findings indicate that few-shot inference with very large LLMs often does not outperform fine-tuning smaller pre-trained models, underscoring the importance of task-specific model optimization.Additionally, we investigated different training methodologies such as single-task training, mixed-task training, and LoRA merging both within domain/tasks and between different tasks. Through rigorous experimentation and analysis, this paper offers valuable insights into the potential effectiveness of LLMs to advance natural language processing capabilities within the e-commerce industry.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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