[Paper Review] Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)

Tong Zhan, Chenxi Shi+3 more|arXiv (Cornell University)|May 16, 2024

Advanced Computing and Algorithms4 citations

TL;DR

This paper investigates fine-tuning techniques to optimize GPT-3 for sentiment analysis, demonstrating that fine-tuning significantly improves performance over prompt-based inference. The study confirms that fine-tuned GPT-3 achieves superior accuracy and robustness on sentiment classification tasks, offering a practical framework for leveraging large language models in NLP applications.

ABSTRACT

With the rapid development of natural language processing (NLP) technology, large-scale pre-trained language models such as GPT-3 have become a popular research object in NLP field. This paper aims to explore sentiment analysis optimization techniques based on large pre-trained language models such as GPT-3 to improve model performance and effect and further promote the development of natural language processing (NLP). By introducing the importance of sentiment analysis and the limitations of traditional methods, GPT-3 and Fine-tuning techniques are introduced in this paper, and their applications in sentiment analysis are explained in detail. The experimental results show that the Fine-tuning technique can optimize GPT-3 model and obtain good performance in sentiment analysis task. This study provides an important reference for future sentiment analysis using large-scale language models.

Motivation & Objective

• To address the limitations of traditional sentiment analysis methods by leveraging large pre-trained language models like GPT-3.
• To evaluate the effectiveness of fine-tuning techniques in improving sentiment classification performance on GPT-3.
• To provide a practical framework for deploying optimized LLMs in real-world sentiment analysis applications.
• To compare prompt-based inference with fine-tuned GPT-3 in terms of accuracy and robustness on sentiment tasks.

Proposed method

• The study employs GPT-3 as the base large language model for sentiment analysis tasks.
• Fine-tuning is applied to adapt GPT-3 to specific sentiment classification datasets.
• The model is fine-tuned on labeled sentiment data using standard optimization techniques such as learning rate scheduling and batch training.
• Performance is evaluated using standard NLP metrics like accuracy, F1-score, and precision/recall on benchmark datasets.
• Hyperparameter tuning is conducted to optimize the fine-tuning process for improved generalization.
• Comparative experiments are performed between zero-shot prompt-based inference and fine-tuned GPT-3.

Experimental results

Research questions

• RQ1 How does fine-tuning improve GPT-3’s performance in sentiment analysis compared to prompt-based inference?
• RQ2 What are the key hyperparameters that influence fine-tuning effectiveness for sentiment classification?
• RQ3 How does the fine-tuned GPT-3 model generalize across different sentiment datasets?
• RQ4 What are the performance gains of fine-tuning over standard prompt engineering in sentiment analysis?

Key findings

• Fine-tuning significantly improves GPT-3’s sentiment classification accuracy compared to zero-shot prompting.
• The fine-tuned model achieves higher F1-scores and better precision/recall on benchmark sentiment datasets.
• Optimal performance is observed with careful hyperparameter tuning, particularly learning rate and batch size.
• Fine-tuned GPT-3 demonstrates improved robustness and consistency across diverse sentiment classification tasks.