Adapting to dynamic data distributions is a practical yet challenging task.

Large language models (LLMs) have rapidly improved text embeddings for a growing array of natural-language processing tasks.

Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.

Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm.

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented.

Recently, commonsense reasoning in text generation has attracted much attention. Generative commonsense reasoning is the task that requires machines, given a group of keywords, to compose a single coherent sentence with commonsense plausibility. While existing datasets targeting generative commonsense reasoning focus on everyday scenarios, it is unclear how well machines reason under specific geographical and temporal contexts.

Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question ( i .