Summary of Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts, by Naimeng Ye and Hongseok Namkoong
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
by Naimeng Ye, Hongseok Namkoong
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 A study on intelligent agents demonstrates that pre-trained sequence models can naturally perform probabilistic reasoning over exchangeable data points. These models form informed beliefs and sharpen them as they gather more information, unlike typical Bayesian models that quantify uncertainty over latent parameters. The research illustrates how sequence modeling provides a valid Bayesian model by leveraging De Finetti’s predictive view of probabilistic reasoning. Pre-training autoregressive models is equivalent to forming informed beliefs based on prior observations, while forward generation simulates instantiations of an environment. The study shows that exchangeable sequence models can explicitly perform statistical inference and capture epistemic uncertainty over latent environments through predicted future observations. Furthermore, the sequence prediction loss controls the quality of uncertainty quantification, and approaches for encoding exchangeability in sequence model architectures include data augmentation, regularization, and causal masking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Intelligent agents need to be able to understand their own uncertainty. A new study shows that special types of language models can do this naturally. These models form ideas about what might happen based on the information they have so far. They also get better at guessing what will happen as they learn more. This is different from other ways of thinking about probability, which are based on hidden factors or patterns. The study shows that these language models can be used to make predictions and understand how likely certain events are. It also proposes new ways to train these models so they can do this better. |
Keywords
» Artificial intelligence » Autoregressive » Data augmentation » Inference » Probability » Regularization » Sequence model
