Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)
<|reference_start|>Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3): 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.<|reference_end|>
@article{zhan2024optimization,
    title={Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)},
    author={Tong Zhan, Chenxi Shi, Yadong Shi, Huixiang Li, Yiyu Lin},
    journal={arXiv preprint arXiv:2405.09770},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09770},
    primaryClass={cs.CL cs.AI}
}