10 citations · 21 across the 10 of their papers we have counts for
10 papers
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
Pengcheng Jiang, Lang Cao, Ruike Zhu +5
Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to the unstructured nature of…
Taxonomy-guided Semantic Indexing for Academic Paper Search
SeongKu Kang, Yunyi Zhang, Pengcheng Jiang +3
Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often strug…
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
Pengcheng Jiang, Cao Xiao, Minhao Jiang +4
Large language models (LLMs) have demonstrated significant potential in clinical decision support. Yet LLMs still suffer from hallucinations and lack fine-grained contextual medica…
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Pengcheng Jiang, Lang Cao, Cao Xiao +3
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and…
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
Pengcheng Jiang, Cao Xiao, Zifeng Wang +3
The advent of large language models (LLMs) has significantly advanced natural language processing tasks like text summarization. However, their large size and computational demands…
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
Pengcheng Jiang, Jiacheng Lin, Zifeng Wang +2
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). How…
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations
Pengcheng Jiang, Cao Xiao, Tianfan Fu +4
Molecular representation learning is vital for various downstream applications, including the analysis and prediction of molecular properties and side effects. While Graph Neural N…
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
Pengcheng Jiang, Shivam Agarwal, Bowen Jin +3
The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarg…
GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs
Pengcheng Jiang, Cao Xiao, Adam Cross +1
Clinical predictive models often rely on patients' electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging. Thi…
OACAL: Finding Module-consistent Specifications to Secure Systems from Weakened User Obligations
Pengcheng Jiang, Kenji Tei
Users interacting with a system through UI are typically obliged to perform their actions in a pre-determined order, to successfully achieve certain functional goals. However, such…