Large Language Models For Text Classification: Case Study And Comprehensive Review

• URL: http://arxiv.org/abs/2501.08457v1
• Date: Tue, 14 Jan 2025 22:02:38 GMT
• Title: Large Language Models For Text Classification: Case Study And Comprehensive Review
• Authors: Arina Kostina, Marios D. Dikaiakos, Dimosthenis Stefanidis, George Pallis,
• Abstract summary: We evaluate the performance of different Large Language Models (LLMs) in comparison with state-of-the-art deep-learning and machine-learning models. Our work reveals significant variations in model responses based on the prompting strategies.
• Score: 0.3428444467046467
• License:
• Abstract: Unlocking the potential of Large Language Models (LLMs) in data classification represents a promising frontier in natural language processing. In this work, we evaluate the performance of different LLMs in comparison with state-of-the-art deep-learning and machine-learning models, in two different classification scenarios: i) the classification of employees' working locations based on job reviews posted online (multiclass classification), and 2) the classification of news articles as fake or not (binary classification). Our analysis encompasses a diverse range of language models differentiating in size, quantization, and architecture. We explore the impact of alternative prompting techniques and evaluate the models based on the weighted F1-score. Also, we examine the trade-off between performance (F1-score) and time (inference response time) for each language model to provide a more nuanced understanding of each model's practical applicability. Our work reveals significant variations in model responses based on the prompting strategies. We find that LLMs, particularly Llama3 and GPT-4, can outperform traditional methods in complex classification tasks, such as multiclass classification, though at the cost of longer inference times. In contrast, simpler ML models offer better performance-to-time trade-offs in simpler binary classification tasks.

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