Abstract Large Language Models (LLMs) have shown promising performance in knowledge-
intensive reasoning tasks that require a compound understanding of knowledge. However …

Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

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

Previous studies on federated learning (FL) often encounter performance degradation due
to data heterogeneity among different clients. In light of the recent advances in multimodal …

Large Language Models (LLMs) have showcased exceptional capabilities in various
domains, attracting significant interest from both academia and industry. Despite their …

Abstract Large Language Models (LLMs) can teach small language models (SLMs) to solve
complex reasoning tasks (eg, mathematical question answering) by Chain-of-thought …

Models for low-latency, streaming applications could benefit from the knowledge capacity of
larger models, but edge devices cannot run these models due to resource constraints. A …

Event temporal relation (TempRel) is a primary subject of the event relation extraction task.
However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise …

Large language models (LLMs) have showcased remarkable skills, rivaling or even
exceeding human intelligence in certain areas. Their proficiency in translation is notable, as …