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This paper focuses on the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model, and builds on state space models (SSMs), which require only linear inference time given the input.
We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This problem is closely related to in-context learning and language model inference. Since the inference cost of a transformer model scales quadratically with sequence length, th...
This work develops an assumption-lean partial identification framework that uses such measurements as weak shadow variables, defined as outcome-informative proxies that are conditionally independent of missingness given the true outcome and observed covariates.
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