Summary of Optimization Techniques For Sentiment Analysis Based on Llm (gpt-3), by Tong Zhan et al.
Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)
by Tong Zhan, Chenxi Shi, Yadong Shi, Huixiang Li, Yiyu Lin
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to optimize sentiment analysis techniques using large pre-trained language models like GPT-3, aiming to improve model performance and promote NLP development. The study introduces the importance of sentiment analysis, traditional methods’ limitations, and how GPT-3 and Fine-tuning techniques can be applied for better results. Experimental findings show that Fine-tuning optimizes the GPT-3 model, achieving good performance in sentiment analysis tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to improve sentiment analysis using large language models like GPT-3. It’s important because it helps us understand how people feel about things online. Currently, traditional methods are not very effective, so scientists are trying new approaches. This study shows that a technique called Fine-tuning can make the GPT-3 model work better for sentiment analysis tasks. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Nlp
