8 citations · 25 across the 17 of their papers we have counts for
9 papers · 1 filter
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities
Ming Zhang, Caishuang Huang, Yilong Wu +10
Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection. How to utilize TOD accurately, efficiently and effecti…
Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning
Jun Zhao, Jingqi Tong, Yurong Mou +3
Human cognition exhibits systematic compositionality, the algebraic ability to generate infinite novel combinations from finite learned components, which is the key to understandin…
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models
Wei He, Shichun Liu, Jun Zhao +6
Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot…
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Yi Lu, Xin Zhou, Wei He +5
Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length…
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
Jun Zhao, Can Zu, Hao Xu +6
Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks. However, LLMs with long context windows have…
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution
Nuo Xu, Jun Zhao, Can Zu +9
Faithfulness, expressiveness, and elegance is the constant pursuit in machine translation. However, traditional metrics like \textit{BLEU} do not strictly align with human preferen…
Unveiling Linguistic Regions in Large Language Models
Zhihao Zhang, Jun Zhao, Qi Zhang +2
Large Language Models (LLMs) have demonstrated considerable cross-lingual alignment and generalization ability. Current research primarily focuses on improving LLMs' cross-lingual…
DE-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
Jianing He, Qi Zhang, Weiping Ding +4
Early exiting has demonstrated its effectiveness in accelerating the inference of pre-trained language models like BERT by dynamically adjusting the number of layers executed. Howe…
LLaMA Beyond English: An Empirical Study on Language Capability Transfer
Jun Zhao, Zhihao Zhang, Luhui Gao +3
In recent times, substantial advancements have been witnessed in large language models (LLMs), exemplified by ChatGPT, showcasing remarkable proficiency across a range of complex t…