591 citations
- Chang Gung Memorial HospitalTW10 papers
- Shenzhen Technology UniversityCN9 papers
- National Institutes of Health Clinical CenterUS8 papers
- Linkou Chang Gung Memorial HospitalTW6 papers
- National Institutes of HealthUS6 papers
- Beijing University of Posts and TelecommunicationsCN5 papers
- Huazhong University of Science and TechnologyCN5 papers
- University of Science and Technology of ChinaCN5 papers
- Beijing Institute of TechnologyCN4 papers
- Alibaba Group (China)CN3 papers
- Johns Hopkins UniversityUS3 papers
- New York Life Insurance Company (United States)US3 papers
16 papers · 1 filter
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents
Zhigen Li, Jianxiang Peng, Yanmeng Wang +13
Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of…
Enhancing Low-Resource Relation Representations through Multi-View Decoupling
Chenghao Fan, Wei Wei, Xiaoye Qu +4
Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource sce…
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning
Yusheng Su, Chi-Min Chan, Jiali Cheng +9
Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by training only minimal parameters. Different PET methods utilize…
Improving Personality Consistency in Conversation by Persona Extending
Yifan Liu, Wei Wei, Jiayi Liu +3
Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions. However, existing personalized approaches commonly generate respon…
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
Yanmeng Wang, Jun Bai, Ye Wang +5
Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of ques…
RefBERT: Compressing BERT by Referencing to Pre-computed Representations
Xinyi Wang, Haiqin Yang, Liang Zhao +2
Recently developed large pre-trained language models, e.g., BERT, have achieved remarkable performance in many downstream natural language processing applications. These pre-traine…