This reminds me of the paper Chris linked as well. I think there’s very solid evidence on the relationship between the kind of meta learning Transformers go through and Bayesian inference (e.g., see this, this, and this). The main question I have been thinking about is what is a state for language and how that can be useful if so discovered in this way? For state-based RL/control tasks this seems relatively straightforward (e.g., see this and this), but this is much less clear for more abstract tasks. It’d be great to hear your thoughts!
The figures remind me of figures 3 and 4 from Meta-learning of Sequential Strategies, Ortega et al 2019, which also study how autoregressive models (RNNs) infer underlying structure. Could be a good reference to check out!
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this looks highly relevant! thanks!
This reminds me of the paper Chris linked as well. I think there’s very solid evidence on the relationship between the kind of meta learning Transformers go through and Bayesian inference (e.g., see this, this, and this). The main question I have been thinking about is what is a state for language and how that can be useful if so discovered in this way? For state-based RL/control tasks this seems relatively straightforward (e.g., see this and this), but this is much less clear for more abstract tasks. It’d be great to hear your thoughts!