Papers with LogME
Evidence > Intuition: Transferability Estimation for Encoder Selection (2022.emnlp-main)
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| Challenge: | Existing studies on LM transferability have focused on a priori tuning of encoders . prior work has examined the different yet related tasks of performance prediction . |
| Approach: | They propose to generate quantitative evidence to predict which LM will perform best on a target task without fine-tuning all candidates. |
| Outcome: | The proposed model outperforms the standard of human practitioner ranking in 94% of the setups. |
Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks (2024.findings-acl)
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| Challenge: | Existing models for NLP tasks require fine-tuning, but it is computationally infeasible. |
| Approach: | They propose an approach that inexpensively estimates a ranking of the expected performance of a given set of transformer language models for a specific task. |
| Outcome: | The proposed model improves the Pearson correlation coefficient between the true model ranks and the estimate. |