Summary of Selecting Between Bert and Gpt For Text Classification in Political Science Research, by Yu Wang et al.
Selecting Between BERT and GPT for Text Classification in Political Science Research
by Yu Wang, Wen Qu, Xin Ye
First submitted to arxiv on: 7 Nov 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 This study investigates the potential of GPT-based models combined with prompt engineering as a viable alternative to fine-tuned BERT models in addressing data scarcity in text classification. The authors conduct experiments across various classification tasks, evaluating the effectiveness of BERT-based and GPT-based models in low-data scenarios. While zero-shot and few-shot learning with GPT models show promise for early-stage research exploration, they generally fall short or match the performance of BERT fine-tuning, particularly as the training set size increases (e.g., 1,000 samples). The study concludes by comparing these approaches in terms of performance, ease of use, and cost, providing practical guidance for researchers facing data limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make computers better at understanding text when they don’t have much information. The authors test different kinds of models that can be used with or without extra training data. They find that a special kind of model called BERT is still the best choice when you have enough data, but other models might be useful when you’re working with very little data. The study shows how these different approaches compare and what they’re good for. |
Keywords
» Artificial intelligence » Bert » Classification » Few shot » Fine tuning » Gpt » Prompt » Text classification » Zero shot
