No More Distractions: an Adaptive Up-Sampling Algorithm to Reduce Data Artifacts
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910307765452800 |
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| author | Chen, Han |
| author_facet | Chen, Han |
| contents | Researchers recently found out that sometimes language models achieve high accuracy on benchmark data set, but they can not generalize very well with even little changes to the original data set. This is sometimes due to data artifacts, model is learning the spurious correlation between tokens and labels, instead of the semantics and logic. In this work, we analyzed SNLI data and visualized such spurious correlations. We proposed an adaptive up-sampling algorithm to correct the data artifacts, which is simple and effective, and does not need human edits or annotation. We did an experiment applying the algorithm to fix the data artifacts in SNLI data and the model trained with corrected data performed significantly better than the model trained with raw SNLI data, overall, as well as on the subset we corrected. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13907 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | No More Distractions: an Adaptive Up-Sampling Algorithm to Reduce Data Artifacts Chen, Han Computation and Language Researchers recently found out that sometimes language models achieve high accuracy on benchmark data set, but they can not generalize very well with even little changes to the original data set. This is sometimes due to data artifacts, model is learning the spurious correlation between tokens and labels, instead of the semantics and logic. In this work, we analyzed SNLI data and visualized such spurious correlations. We proposed an adaptive up-sampling algorithm to correct the data artifacts, which is simple and effective, and does not need human edits or annotation. We did an experiment applying the algorithm to fix the data artifacts in SNLI data and the model trained with corrected data performed significantly better than the model trained with raw SNLI data, overall, as well as on the subset we corrected. |
| title | No More Distractions: an Adaptive Up-Sampling Algorithm to Reduce Data Artifacts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.13907 |