The proliferation of wearable technology enables the generation of vast amounts of sensor
data, offering significant opportunities for advancements in health monitoring, activity …

Large Language Models (LLMs) represent a significant advancement in artificial
intelligence, finding applications across various domains. However, their reliance on …

Large scale language models (LLM) have received significant attention and found diverse
applications across various domains, but their development encounters challenges in real …

Swift advancements in large language model (LLM) technologies lead to widespread
research and applications, particularly in integrating LLMs with auxiliary tools, known as tool …

Language models produce a distribution over the next token; can we use this information to
recover the prompt tokens? We consider the problem of language model inversion and …

Adapting Large Language Models (LLMs) to specific tasks introduces concerns about
computational efficiency, prompting an exploration of efficient methods such as In-Context …

Large Language Models (LLMs) have emerged as dominant tools for various tasks,
particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns …

The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS)
requires more comprehensive and accurate data, which imposes heightened demands on …

Large language models (LLMs) have exhibited remarkable efficacy and proficiency in a
wide array of NLP endeavors. Nevertheless, concerns are growing rapidly regarding the …

In-context learning (ICL) is an important capability of Large Language Models (LLMs),
enabling these models to dynamically adapt based on specific, in-context exemplars …