activity
20162023
most citedThe Rise and Potential of Large Language Model Based Agents: A Survey

256 citations · 525 across the 25 of their papers we have counts for

collaborators

18 papers

cs.CL20231 cited

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…

cs.CL20231 cited

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…

cs.CL20237 cited

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…

cs.CL2023

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…

cs.CL20234 cited

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…

cs.CL20233 cited

Improving Contrastive Learning of Sentence Embeddings from AI Feedback

Qinyuan Cheng, Xiaogui Yang, Tianxiang Sun +2

Contrastive learning has become a popular approach in natural language processing, particularly for the learning of sentence embeddings. However, the discrete nature of natural lan…