7 citations · 9 across the 5 of their papers we have counts for
7 papers · 1 filter
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
Zheng Chu, Jingchang Chen, Qianglong Chen +6
Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval…
Knowledge-enhanced Memory Model for Emotional Support Conversation
Mengzhao Jia, Qianglong Chen, Liqiang Jing +2
The prevalence of mental disorders has become a significant issue, leading to the increased focus on Emotional Support Conversation as an effective supplement for mental health sup…
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Chenin Li, Qianglong Chen, Yin Zhang +2
Commonsense reasoning is a pivotal skill for large language models, yet it presents persistent challenges in specific tasks requiring this competence. Traditional fine-tuning appro…
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering
Qianglong Chen, Guohai Xu, Ming Yan +4
Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases. However, limited by the pro…
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference
Qianglong Chen, Feng Ji, Feng-Lin Li +4
Knowledge distillation is of key importance to launching multilingual pre-trained language models for real applications. To support cost-effective language inference in multilingua…
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning
Qianglong Chen, Feng-Lin Li, Guohai Xu +3
Although pre-trained language models (PLMs) have achieved state-of-the-art performance on various natural language processing (NLP) tasks, they are shown to be lacking in knowledge…