AI RESEARCH
LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information
arXiv CS.LG
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ArXi:2605.13188v1 Announce Type: cross Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generating answers under incomplete context can be viewed as an implicit imputer, and evaluated against a criterion from the multiple imputation (MI) literature: uncertainty should scale with the amount of missing information. We assess this criterion on SQuAD, using a controlled framework in which context availability is varied across five levels.