Summary of Comparing Template-based and Template-free Language Model Probing, by Sagi Shaier et al.
Comparing Template-based and Template-free Language Model Probing
by Sagi Shaier, Kevin Bennett, Lawrence E Hunter, Katharina von der Wense
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 investigates the differences between using expert-made templates and naturally-occurring text as cloze-task language model (LM) probes. It evaluates 16 LMs on 10 English datasets in general and biomedical domains to answer three research questions: Do model rankings differ between template-based and template-free approaches? Do models’ absolute scores differ? And do the answers change between general and domain-specific models? The findings show that template-free and template-based approaches often rank models differently, except for top domain-specific models. Scores decrease by up to 42% when comparing parallel prompts. Perplexity is negatively correlated with accuracy in template-free probing, but positively correlated in template-based probing. Models tend to predict the same answers frequently across prompts in template-based probing, which is less common with template-free techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how language models perform on tasks that involve filling in the blanks of text. It wants to know if using expert-made templates or real-life sentences affects the results. The researchers tested 16 language models on 10 datasets and found some interesting differences. They discovered that different approaches can rank models differently, and that scores can be much lower when using one approach over another. They also found that how well a model does at predicting the right answer is connected to its ability to understand the text in both approaches. |
Keywords
* Artificial intelligence * Language model * Perplexity
