256 citations · 569 across the 43 of their papers we have counts for
19 papers · 1 filter
Can AI Assistants Know What They Don't Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu +7
Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Alt…
Optimizing Non-Autoregressive Transformers with Contrastive Learning
Chenxin An, Jiangtao Feng, Fei Huang +2
Non-autoregressive Transformers (NATs) reduce the inference latency of Autoregressive Transformers (ATs) by predicting words all at once rather than in sequential order. They have…
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Yuqing Yang, Qipeng Guo, Xiangkun Hu +3
Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of compl…
Do Large Language Models Know What They Don't Know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo +3
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their pe…
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System
Shimin Li, Xiaotian Zhang, Yanjun Zheng +2
Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately. We discover that data utilization efficiency…
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
Mozhi Zhang, Hang Yan, Yaqian Zhou +1
Few-shot Named Entity Recognition (NER) is a task aiming to identify named entities via limited annotated samples. Recently, prototypical networks have shown promising performance…