Investigating LLM Applications in E-Commerce
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...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
22.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2408.12779 |