ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909642117873664 |
|---|---|
| author | Zheng, Kangjie Yang, Junwei Liang, Siyue Feng, Bin Liu, Zequn Ju, Wei Xiao, Zhiping Zhang, Ming |
| author_facet | Zheng, Kangjie Yang, Junwei Liang, Siyue Feng, Bin Liu, Zequn Ju, Wei Xiao, Zhiping Zhang, Ming |
| contents | Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input sequences with [MASK] tokens and learning to reconstruct the original content based on the remaining context. This paper explores the impact of [MASK] tokens on MLMs. Analytical studies show that masking tokens can introduce the corrupted semantics problem, wherein the corrupted context may convey multiple, ambiguous meanings. This problem is also a key factor affecting the performance of MLMs on downstream tasks. Based on these findings, we propose a novel enhanced-context MLM, ExLM. Our approach expands [MASK] tokens in the input context and models the dependencies between these expanded states. This enhancement increases context capacity and enables the model to capture richer semantic information, effectively mitigating the corrupted semantics problem during pre-training. Experimental results demonstrate that ExLM achieves significant performance improvements in both text modeling and SMILES modeling tasks. Further analysis confirms that ExLM enriches semantic representations through context enhancement, and effectively reduces the semantic multimodality commonly observed in MLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13397 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models Zheng, Kangjie Yang, Junwei Liang, Siyue Feng, Bin Liu, Zequn Ju, Wei Xiao, Zhiping Zhang, Ming Computation and Language Machine Learning Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input sequences with [MASK] tokens and learning to reconstruct the original content based on the remaining context. This paper explores the impact of [MASK] tokens on MLMs. Analytical studies show that masking tokens can introduce the corrupted semantics problem, wherein the corrupted context may convey multiple, ambiguous meanings. This problem is also a key factor affecting the performance of MLMs on downstream tasks. Based on these findings, we propose a novel enhanced-context MLM, ExLM. Our approach expands [MASK] tokens in the input context and models the dependencies between these expanded states. This enhancement increases context capacity and enables the model to capture richer semantic information, effectively mitigating the corrupted semantics problem during pre-training. Experimental results demonstrate that ExLM achieves significant performance improvements in both text modeling and SMILES modeling tasks. Further analysis confirms that ExLM enriches semantic representations through context enhancement, and effectively reduces the semantic multimodality commonly observed in MLMs. |
| title | ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2501.13397 |