Transformers have recently revolutionized many domains in modern machine learning and
one salient discovery is their remarkable in-context learning capability, where models can …

Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet
there is ongoing debate about these abilities and the potential data contamination problem …

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) are transforming the way people generate, explore, and
engage with content. We study how we can develop LLM applications for online social …

Large monolithic generative models trained on massive amounts of data have become an
increasingly dominant approach in AI research. In this paper, we argue that we should …

The high cost of full-parameter fine-tuning (FFT) of Large Language Models (LLMs) has led
to a series of parameter-efficient fine-tuning (PEFT) methods. However, it remains unclear …

Large Language Models (LLMs) have shown remarkable proficiency in language
understanding and have been successfully applied to a variety of real-world tasks through …

Despite the widespread adoption of prompting, prompt tuning and prefix-tuning of
transformer models, our theoretical understanding of these fine-tuning methods remains …

Transformers have exhibited impressive in-context learning (ICL) capabilities: they can
generate predictions for new query inputs based on sequences of inputs and outputs (ie …

This paper explores the role of artificial intelligence (AI) within economic institutions,
focusing on bounded rationality as understood by Herbert Simon. Artificial Intelligence can …