236 citations · 862 across the 49 of their papers we have counts for
8 papers · 1 filter
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Weize Chen, Yusheng Su, Jingwei Zuo +13
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-…
Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning
Weize Chen, Xu Han, Yankai Lin +3
Parameter-efficient tuning methods (PETs) have achieved promising results in tuning large pre-trained language models (PLMs). By formalizing frozen PLMs and additional tunable para…
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
Yujia Qin, Zihan Cai, Dian Jin +12
Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two proced…
Recyclable Tuning for Continual Pre-training
Yujia Qin, Cheng Qian, Xu Han +6
Continual pre-training is the paradigm where pre-trained language models (PLMs) continually acquire fresh knowledge from growing data and gradually get upgraded. Before an upgraded…
Emergent Modularity in Pre-trained Transformers
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin +7
This work examines the presence of modularity in pre-trained Transformers, a feature commonly found in human brains and thought to be vital for general intelligence. In analogy to…
Plug-and-Play Knowledge Injection for Pre-trained Language Models
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin +8
Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy ne…
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation
Qiu Ran, Yankai Lin, Peng Li +1
Non-autoregressive neural machine translation (NAT) predicts the entire target sequence simultaneously and significantly accelerates inference process. However, NAT discards the de…
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Jie Zhou, Shengding Hu, Xin Lv +6
A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human'…