On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

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 …

The proliferation of large language models (LLMs) requires robust evaluation of their
alignment with local values and ethical standards, especially as existing benchmarks often …

Large language models (LLMs) have revolutionized natural language processing with their
exceptional capabilities. However, deploying LLMs on resource-constrained edge devices …

Pre-trained large language models (LLMs) require fine-tuning to improve their
responsiveness to natural language instructions. Federated learning (FL) offers a way to …

When mobile meets LLMs, mobile app users deserve to have more intelligent usage
experiences. For this to happen, we argue that there is a strong need to apply LLMs for the …

To enhance the efficiency of language models, it would involve optimizing their training and
inference processes to reduce computational demands while maintaining high performance …

As the adoption of large language models increases and the need for per-user or per-task
model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low …

Entity matching (EM) is the problem of determining whether two records refer to same real-
world entity, which is crucial in data integration, eg, for product catalogs or address …

Transformer-based large language models (LLMs) such as Generative Pre-trained
Transformer (GPT) have become popular due to their remarkable performance across …