Large language models (LLMs) have received considerable attention recently due to their
outstanding comprehension and reasoning capabilities, leading to great progress in many …

Large language models (LLMs) often generate content that contains factual errors when
responding to fact-seeking prompts on open-ended topics. To benchmark a model's long …

Abstract Large Language Models have demonstrated remarkable performance across
various tasks exhibiting the capacity to swiftly acquire new skills such as through In-Context …

Large language models (LLMs) have emerged as powerful tools for many AI problems and
exhibit remarkable in-context learning (ICL) capabilities. Compositional ability, solving …

The immense evolution in Large Language Models (LLMs) has underscored the importance
of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from …

We need to trust robots that use often opaque AI methods. They need to explain themselves
to us, and we need to trust their explanation. In this regard, explainability plays a critical role …

Spurred by advancements in scale, large language models (LLMs) have demonstrated
strong few-shot learning ability via in-context learning (ICL). However, the performance of …

Task semantics can be expressed by a set of input-output examples or a piece of textual
instruction. Conventional machine learning approaches for natural language processing …