12 citations · 12 across the 16 of their papers we have counts for
13 papers · 1 filter
Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation
Raymond Li, Md Tawkat Islam Khondaker, Amirhossein Abaskohi +3
Retrieval-augmented generation (RAG) increasingly requires models to answer questions from multiple retrieved documents, where only some sources are relevant and the retrieved bund…
MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval
Amirhossein Abaskohi, Raymond Li, Gaetano Cimino +3
Retrieval-augmented generation (RAG) systems depend critically on how documents are chunked and searched. Fine-grained chunks can improve retrieval precision but expand the search…
Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing
Kai Wei, Raymond Li, Xi Zhu +4
Retrieval-Augmented Generation (RAG) has emerged as a foundational paradigm for grounding large language models in external knowledge. While adaptive retrieval mechanisms have impr…
Improving Topic Modeling by Distilling Soft Labels from Language Models
Raymond Li, Amirhossein Abaskohi, Chuyuan Li +2
Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with…
CEMTM: Contextual Embedding-based Multimodal Topic Modeling
Amirhossein Abaskohi, Raymond Li, Chuyuan Li +2
We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and i…
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection
Chuyuan Li, Raymond Li, Thalia S. Field +1
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzing linguistic abnormalities. In…