1 citations · 1 across the 4 of their papers we have counts for
5 papers
The 3rd Place Solution of CCIR CUP 2025: A Framework for Retrieval-Augmented Generation in Multi-Turn Legal Conversation
Da Li, Zecheng Fang, Qiang Yan +2
Retrieval-Augmented Generation has made significant progress in the field of natural language processing. By combining the advantages of information retrieval and large language mo…
TrustRAG: An Information Assistant with Retrieval Augmented Generation
Yixing Fan, Qiang Yan, Wenshan Wang +3
\Ac{RAG} has emerged as a crucial technique for enhancing large models with real-time and domain-specific knowledge. While numerous improvements and open-source tools have been pro…
IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization
Jie Cao, Dian Jiao, Yang Dai +3
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. The advent of large lan…
Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection
Leihang Zhang, Jiapeng Liu, Qiang Yan
It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than g…
Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding
Shanshan Wang, Zhumin Chen, Zhaochun Ren +3
Pre-trained language models (PLM) have demonstrated their effectiveness for a broad range of information retrieval and natural language processing tasks. As the core part of PLM, m…