Prior work suggests that sentiment can often be captured by approximately linear directions in LLM activation spaces, but a single direction may not fully capture sentiment representations. In natural communication, sentiment is shaped not only by polarity but also by usage factors, such as tone and audience adaptation …
Tables serve as a foundational medium for representing structured information and are widely adopted across diverse domains such as scientific research, financial analysis, public administration, and data-intensive industrial applications. Existing large language models (LLMs)-based table understanding approaches commo …
Entity alignment is a fundamental step for knowledge graph (KG) integration, yet the complex and heterogeneous structures of real-world KGs make accurate alignment particularly challenging. KG entity alignment faces two main challenges. First, representations learned in a single Euclidean or hyperbolic space struggle t …
Low-Rank Adaptation (LoRA) has become a mainstream technique for fine-tuning large language models (LLMs) on downstream tasks due to its memory efficiency. However, traditional inference architectures suffer from computational redundancy and low GPU utilization when serving multi-tenant LoRA models. To address these in …
Abstract Real-world text classification often faces the challenge of long-tailed data distributions, where most categories contain only a few samples. Directly fine-tuning pre-trained language models (PLMs) on such imbalanced data typically causes semantic space collapse for tail classes and severe overfitting to head …
Current research on evidence sentences is aimed at developing document-level relational extraction models with improved interpretability. Evidence sentences extracted using existing methods are often incomplete, leading to poor relationship prediction accuracy. To address this problem, we developed a novel efficient he …