Comparing Template-based and Template-free Language Model Probing
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912094543151104 |
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| author | Shaier, Sagi Bennett, Kevin Hunter, Lawrence E von der Wense, Katharina |
| author_facet | Shaier, Sagi Bennett, Kevin Hunter, Lawrence E von der Wense, Katharina |
| contents | The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 different LMs on 10 probing English datasets -- 4 template-based and 6 template-free -- in general and biomedical domains to answer the following research questions: (RQ1) Do model rankings differ between the two approaches? (RQ2) Do models' absolute scores differ between the two approaches? (RQ3) Do the answers to RQ1 and RQ2 differ between general and domain-specific models? Our findings are: 1) Template-free and template-based approaches often rank models differently, except for the top domain-specific models. 2) Scores decrease by up to 42% Acc@1 when comparing parallel template-free and template-based prompts. 3) Perplexity is negatively correlated with accuracy in the template-free approach, but, counter-intuitively, they are positively correlated for template-based probing. 4) Models tend to predict the same answers frequently across prompts for template-based probing, which is less common when employing template-free techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparing Template-based and Template-free Language Model Probing Shaier, Sagi Bennett, Kevin Hunter, Lawrence E von der Wense, Katharina Computation and Language Machine Learning The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 different LMs on 10 probing English datasets -- 4 template-based and 6 template-free -- in general and biomedical domains to answer the following research questions: (RQ1) Do model rankings differ between the two approaches? (RQ2) Do models' absolute scores differ between the two approaches? (RQ3) Do the answers to RQ1 and RQ2 differ between general and domain-specific models? Our findings are: 1) Template-free and template-based approaches often rank models differently, except for the top domain-specific models. 2) Scores decrease by up to 42% Acc@1 when comparing parallel template-free and template-based prompts. 3) Perplexity is negatively correlated with accuracy in the template-free approach, but, counter-intuitively, they are positively correlated for template-based probing. 4) Models tend to predict the same answers frequently across prompts for template-based probing, which is less common when employing template-free techniques. |
| title | Comparing Template-based and Template-free Language Model Probing |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.00123 |