Summary of Generalization Vs. Memorization in the Presence Of Statistical Biases in Transformers, by John Mitros
Generalization vs. Memorization in the Presence of Statistical Biases in Transformers
by John Mitros
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A study investigates how statistical biases impact transformer models’ ability to generalize to in- and out-of-distribution data on algorithmic tasks. It is found that transformers may overestimate their generalization capabilities due to reliance on spurious correlations. The research evaluates transformer models on synthetic tasks with varying degrees of bias, analyzing the effects of different model components on performance. Results show that statistical biases negatively impact model performance on out-of-distribution data, leading to an overestimation of generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how statistical biases affect how well transformer models can work on new data that’s similar or very different from what they’ve seen before. Some research suggests that transformers might be relying too much on these biases, making them think they’re better at working with new data than they really are. The researchers tested transformer models on some fake tasks to see how they do when there are and aren’t any biases. They also looked at which parts of the model make the biggest difference in its ability to work well. It turns out that these biases can actually make it worse for transformers to work with new data, making them think they’re better than they really are. |
Keywords
» Artificial intelligence » Generalization » Transformer
