Summary of Exploring Prompts to Elicit Memorization in Masked Language Model-based Named Entity Recognition, by Yuxi Xia et al.
Exploring prompts to elicit memorization in masked language model-based named entity recognition
by Yuxi Xia, Anastasiia Sedova, Pedro Henrique Luz de Araujo, Vasiliki Kougia, Lisa Nußbaumer, Benjamin Roth
First submitted to arxiv on: 5 May 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 This paper investigates how prompts affect the ability to detect memorization in masked language models used for named entity recognition tasks. The study employs 400 automatically generated prompts and a pairwise dataset comprising names from the training set and out-of-set names. The authors use these prompts to measure the model’s confidence in predicting names, quantifying prompt performance as the percentage of name pairs where the model shows higher confidence for training set names. The results show that prompt performance varies significantly across models (up to 16 percentage points) and that prompt engineering can increase this gap. Additionally, the study demonstrates that prompt performance is dependent on model architecture but generalizes across different name sets. A comprehensive analysis reveals how prompt properties, contained tokens, and self-attention weights influence prompt performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how prompts affect language models’ ability to learn and remember information. Researchers used many different prompts and tested them on several language models to see which ones worked best. They found that some prompts were much better than others, and that the best prompts can even help improve the model’s performance by 16 percentage points! The study also shows that the type of prompt and the type of model matter, but that certain prompts work well with different models. Overall, this research helps us understand how to use prompts effectively to get language models to do what we want them to do. |
Keywords
» Artificial intelligence » Named entity recognition » Prompt » Self attention
