Summary of A Comprehensive Evaluation Of Large Language Models on Aspect-based Sentiment Analysis, by Changzhi Zhou et al.
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis
by Changzhi Zhou, Dandan Song, Yuhang Tian, Zhijing Wu, Hao Wang, Xinyu Zhang, Jun Yang, Ziyi Yang, Shuhao Zhang
First submitted to arxiv on: 3 Dec 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 In this paper, researchers explore the capabilities of Large Language Models (LLMs) in natural language processing. The models are capable of powerful reasoning and generation, revolutionizing tasks such as In-Context Learning (ICL), which allows for out-of-the-box execution without fine-tuning. Additionally, Parameter-Efficient Fine-Tuning (PEFT) is introduced as a cost-effective method to achieve excellent performance comparable to full fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, large language models have changed the way we process natural language. These powerful models can do things like understand and generate text, and they’re really good at it! One cool thing about these models is that they can learn new tasks without needing a lot of training data. This is because they use something called “in-context learning”, which lets them figure out what to do by looking at examples. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Parameter efficient
