Summary of Language Models For Text Classification: Is In-context Learning Enough?, by Aleksandra Edwards and Jose Camacho-collados
Language Models for Text Classification: Is In-Context Learning Enough?
by Aleksandra Edwards, Jose Camacho-Collados
First submitted to arxiv on: 26 Mar 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 paper explores the capabilities of large language models in zero- and few-shot settings for text classification tasks. It highlights the benefits of these models in understanding natural language prompts, enabling better generalization across tasks and domains without specific training data. The study compares fine-tuning masked language models with prompting techniques and large language models in zero- and few-shot settings on 16 datasets covering binary, multiclass, and multilabel problems. Results show that fine-tuning smaller and more efficient language models can outperform few-shot approaches of larger language models for text classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models do when they don’t need much training data to classify text. It finds that these models are good at understanding instructions written in natural language, which helps them work well on different tasks and areas without needing specific data. The research compares the performance of large language models with a smaller model that has been trained on some data. They tested this on many datasets covering different types of classification problems. The results show that using smaller models can be better than using bigger ones for certain text classification tasks. |
Keywords
» Artificial intelligence » Classification » Few shot » Fine tuning » Generalization » Prompting » Text classification
