4 citations · 12 across the 21 of their papers we have counts for
7 papers · 1 filter
RADAR: Reasoning as Discrimination with Aligned Representations for LLM-based Knowledge Graph Reasoning
Bo Xue, Yuan Jin, Luoyi Fu +2
Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large Language Models (LLMs). Howe…
Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph
Yutong Zhang, Lixing Chen, Shenghong Li +6
Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks nece…
Good Idea or Not, Representation of LLM Could Tell
Yi Xu, Bo Xue, Shuqian Sheng +6
In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful…
AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing
Huawei Ji, Cheng Deng, Bo Xue +6
With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predomin…
FLAME: Empowering Frozen LLMs for Knowledge Graph Completion
Bo Xue, Yi Xu, Bolei Ma +4
Traditional knowledge graph completion (KGC) methods rely solely on structural information and struggle with sparsity, while Large Language Models (LLMs) address these limitations…
RepEval: Effective Text Evaluation with LLM Representation
Shuqian Sheng, Yi Xu, Tianhang Zhang +7
The era of Large Language Models (LLMs) raises new demands for automatic evaluation metrics, which should be adaptable to various application scenarios while maintaining low cost a…
Is Reference Necessary in the Evaluation of NLG Systems? When and Where?
Shuqian Sheng, Yi Xu, Luoyi Fu +4
The majority of automatic metrics for evaluating NLG systems are reference-based. However, the challenge of collecting human annotation results in a lack of reliable references in…