4 citations · 10 across the 7 of their papers we have counts for
7 papers
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…
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
Tongxu Luo, Jiahe Lei, Fangyu Lei +4
Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks. Nonetheless, the process of updating billions of parameters demands s…
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Qianren Mao, Shaobo Zhao, Jiarui Li +4
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm…
TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering
Fangyu Lei, Tongxu Luo, Pengqi Yang +8
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the…
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
Yixuan Weng, Zhiqi Wang, Huanxuan Liao +4
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to…
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
Minjun Zhu, Yixuan Weng, Shizhu He +2
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are li…