Summary of Do Llms Overcome Shortcut Learning? An Evaluation Of Shortcut Challenges in Large Language Models, by Yu Yuan et al.
Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models
by Yu Yuan, Lili Zhao, Kai Zhang, Guangting Zheng, Qi Liu
First submitted to arxiv on: 17 Oct 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 explores the reliance of Large Language Models (LLMs) on dataset biases, known as shortcuts, for prediction. The authors develop Shortcut Suite, a comprehensive test suite to evaluate the impact of shortcuts on LLMs’ performance. They examine six shortcut types, five evaluation metrics, and four prompting strategies, revealing that larger LLMs rely more heavily on shortcuts, especially under zero-shot and few-shot prompts. Chain-of-thought prompting is found to reduce shortcut reliance, while few-shot prompts generally underperform. The paper also highlights overconfidence in predictions and lower explanation quality when dealing with shortcut-laden datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how Large Language Models (LLMs) use shortcuts in their predictions. Shortcuts are like helpful hints that make it easier for the model to make guesses. But, these shortcuts can actually hurt the model’s ability to understand new things. The researchers created a special test suite to see how well LLMs do with different types of shortcuts and prompts. They found that bigger models rely more on shortcuts and that some prompts are better than others at helping the model learn. Overall, this study helps us understand how LLMs work and what we can do to make them better. |
Keywords
» Artificial intelligence » Few shot » Prompting » Zero shot
