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

This paper compares LLMs with traditional machine learning models for text classification, examining the impact of prompting techniques.

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https://arxiv.org/abs/2501.08457

Methods in this Paper 💡:

→ This paper evaluates various LLMs (Llama2, Llama3, Mistral, GPT-4, etc.) against traditional machine learning models (Naive Bayes, Support Vector Machines) and a state-of-the-art model (RoBERTa) on two classification tasks: fake news detection (binary) and employee review classification (multiclass).

→ The paper investigates how different prompting techniques (Chain-of-Thought, Emotional Prompting, Role-Playing, etc.) affect LLM performance.

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Key Insights from this Paper 🤔:

→ LLMs, especially Llama3 and GPT-4, excel in complex scenarios like multi-class classification but can be slower.

→ Simpler models are faster and perform well in simpler binary classification.

→ Prompting significantly influences LLM performance, with Chain-of-Thought often improving results.

→ Quantized LLMs can match or exceed non-quantized performance.

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Results 💯:

→ Llama3 70B achieved the highest F1-score of 94.4% on fake news detection.

→ GPT-4 achieved the best F1-score of 87.6% in multiclass employee review classification.

→ RoBERTa achieved 93% F1-Score on binary fake news detection in only 4 seconds, outperforming many LLMs.