Summary of Towards Better Understanding Of In-context Learning Ability From In-context Uncertainty Quantification, by Shang Liu et al.
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
by Shang Liu, Zhongze Cai, Guanting Chen, Xiaocheng Li
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 Predicting simple function classes has been a popular testbed for developing theory and understanding of Transformers’ in-context learning (ICL) ability. This paper revisits training Transformers on linear regression tasks, but with a twist – it considers predicting both the conditional expectation [Y|X] and the conditional variance Var(Y|X) as bi-objective predictions. This approach allows for better design of out-of-distribution experiments to distinguish ICL from in-weight learning (IWL), and makes a clearer separation between algorithms with or without prior training distribution information. The paper theoretically shows that the trained Transformer reaches near Bayes-optimum, suggesting the importance of using prior training distribution information. This method can be extended to other cases. Specifically, it proves a generalization bound of () on n tasks with sequences of length T, providing sharper analysis compared to previous results. Empirically, the paper illustrates that while the trained Transformer behaves as Bayes-optimal solution in distribution, it does not necessarily perform Bayesian inference when facing task shifts. The paper also demonstrates the trained Transformer’s ICL ability over covariates shift and prompt-length shift, and interprets them as generalization over a meta distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how Transformers can be used to predict simple relationships between things. Normally, these predictions are based on just one type of information – the average value. But in this case, the researchers want to know both the average value and how much that value can vary. This helps them design better tests to see if the Transformer is really learning or just memorizing the training data. The paper shows that when the Transformer is trained correctly, it becomes very good at making predictions, almost as good as a perfect predictor. However, when the situation changes, the Transformer doesn’t always make the best decision – sometimes it makes mistakes. The researchers also show that the Transformer can be used to make predictions in different situations and that it’s really good at generalizing from one set of data to another. |
Keywords
» Artificial intelligence » Bayesian inference » Generalization » Linear regression » Prompt » Transformer
