Summary of Improving Pre-trained Language Model Sensitivity Via Mask Specific Losses: a Case Study on Biomedical Ner, by Micheal Abaho et al.
Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER
by Micheal Abaho, Danushka Bollegala, Gary Leeming, Dan Joyce, Iain E Buchan
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 proposes a novel approach to adapt language models (LMs) to new domains called Mask Specific Language Modeling (MSLM). This approach aims to address the issue of insensitive fine-tuning, where LMs ignore the differences between source and target domains. MSLM weights the importance of domain-specific terms (DS-terms) during fine-tuning by jointly masking DS-terms and generic words. The paper shows that this approach improves LM sensitivity and detection of DS-terms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to teach language models about different topics. They wanted to help language models understand how to talk about things in a more accurate way, like using medical terms correctly. To do this, they created a technique called Mask Specific Language Modeling (MSLM). MSLM helps language models focus on important words and phrases related to the topic, rather than just copying what it learned from other places. This makes language models better at understanding and talking about new topics. |
Keywords
* Artificial intelligence * Fine tuning * Mask
