Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)
This work addresses sentiment analysis for NLP applications, but it is incremental as it applies standard fine-tuning to an existing model.
The paper tackled sentiment analysis by applying fine-tuning techniques to the GPT-3 model, resulting in improved performance with good results reported in experiments.
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.