3 citations · 7 across the 7 of their papers we have counts for
7 papers
Context-Driven Incremental Compression for Multi-Turn Dialogue Generation
Yeongseo Jung, Jaehyeok Kim, Eunseo Jung +5
Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with conversation length. Naive…
Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR
Yuhang He, Haodong Wu, Siyi Liu +7
Reinforcement Learning with Verifiable Rewards (RLVR) improves the reasoning ability of Large Language Models (LLMs), but sparse outcome rewards make token-level credit assignment…
A Survey of Secure Retrieval-Augmented Generation
Yuming Xu, Mingtao Zhang, Zhuohan Ge +7
Retrieval-augmented generation (RAG) improves large language models (LLMs) with external knowledge, but this access path creates security risks distinct from inherent prompt-only o…
Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning
Tong Li, Jiachuan Wang, Yongqi Zhang +2
Citation classification, which identifies the intention behind academic citations, is pivotal for scholarly analysis. Previous works suggest fine-tuning pretrained language models…
Activation-aware Probe-Query: Effective Key-Value Retrieval for Long-Context LLMs Inference
Qingfa Xiao, Jiachuan Wang, Haoyang Li +6
Recent advances in large language models (LLMs) have showcased exceptional performance in long-context tasks, while facing significant inference efficiency challenges with limited…
R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
Fuda Ye, Shuangyin Li, Yongqi Zhang +1
Retrieval augmented generation (RAG) has been applied in many scenarios to augment large language models (LLMs) with external documents provided by retrievers. However, a semantic…
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation
Yuhong He, Yongqi Zhang, Shizhu He +1
Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. Previous works typically employ a sequence-to-sequence framework to genera…