Summary of Navigating the Shortcut Maze: a Comprehensive Analysis Of Shortcut Learning in Text Classification by Language Models, By Yuqing Zhou et al.
Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models
by Yuqing Zhou, Ruixiang Tang, Ziyu Yao, Ziwei Zhu
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses the limitations of language models (LMs) that rely on spurious correlations, compromising their accuracy and generalizability. The authors introduce a comprehensive benchmark to categorize shortcuts into occurrence, style, and concept, exploring how these shortcuts influence LMs’ performance. They conduct extensive experiments across traditional LMs, large language models, and state-of-the-art robust models to investigate models’ resilience and susceptibility to sophisticated shortcuts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how language models can be tricked by subtle biases in the data they learn from. The researchers create a special test to see how well different types of models do when faced with these biases. They find that even the most advanced models are not immune to these biases and that some are more vulnerable than others. |
