1 citations · 1 across the 5 of their papers we have counts for
5 papers
Concept Based Continuous Prompts for Interpretable Text Classification
Qian Chen, Dongyang Li, Xiaofeng He
Continuous prompts have become widely adopted for augmenting performance across a wide range of natural language tasks. However, the underlying mechanism of this enhancement remain…
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
Taolin Zhang, Dongyang Li, Qizhou Chen +5
Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the halluci…
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models
Dongyang Li, Junbing Yan, Taolin Zhang +5
Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to us…
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
Junbing Yan, Chengyu Wang, Taolin Zhang +5
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding. Previous language models facilitated knowledge acquisition by incorporating knowled…
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
Ruyao Xu, Taolin Zhang, Chengyu Wang +6
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the performance of various downstream NLP tasks by injecting knowledge facts from large-scale Knowledge Graphs (KGs)…