Studying how people interact with large language models (LLMs) in real-world scenarios is
increasingly important due to their widespread use in various applications. In this paper, we …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

Large language models (LLMs) are now available from cloud API providers in various sizes
and configurations. While this diversity offers a broad spectrum of choices, effectively …

Many billion-scale large language models (LLMs) have been released for resource-
constraint mobile devices to provide local LLM inference service when cloud-based …

The rapidly growing number and variety of Large Language Models (LLMs) present
significant challenges in efficiently selecting the appropriate LLM for a given query …

Transformer based large language models have achieved tremendous success. However,
the significant memory and computational costs incurred during the inference process make …

Large-scale deployment of generative AI tools often depends on costly API calls to a Large
Language Model (LLM) to fulfil user queries. To curtail the frequency of these calls, one can …

Large language model (LLM)-based applications consist of both LLM and non-LLM
components, each contributing to the end-to-end latency. Despite great efforts to optimize …

Language models as a service (LMaaS) enable users to accomplish tasks without requiring
specialized knowledge, simply by paying a service provider. However, numerous providers …

Abstract The application of Machine Learning (ML) for predicting graduate student
employability is a growing area of research, driven by the need to align educational …