Summary of Small Language Models Are Good Too: An Empirical Study Of Zero-shot Classification, by Pierre Lepagnol (lisn) et al.
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification
by Pierre Lepagnol, Thomas Gerald, Sahar Ghannay, Christophe Servan, Sophie Rosset
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A study examining the efficiency of large versus small language models for text classification has found that small models can effectively classify texts, often rivaling or surpassing larger counterparts. The researchers tested 15 datasets using language models with parameters ranging from 77 million to 40 billion, exploring different architectures and scoring functions. Their findings challenge the prevailing dominance of large language models and suggest that resource-efficient small models may offer viable solutions for specific data classification challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Small language models can be just as good at classifying texts as larger ones, according to a new study. This is important because it means we might not need to use as much computer power or memory to get the job done. The researchers tested many different types of small and large language models on 15 different datasets. They found that some small models were just as good at classifying texts as larger ones, which could be helpful in certain situations. |
Keywords
» Artificial intelligence » Classification » Text classification
