most citedSurvey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application

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cs.CL20242 cited

Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application

Chuanpeng Yang, Wang Lu, Yao Zhu +5

Large Language Models (LLMs) have showcased exceptional capabilities in various domains, attracting significant interest from both academia and industry. Despite their impressive p…

cs.CL20231 cited

Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling

Hai Yu, Chong Deng, Qinglin Zhang +3

Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues…

cs.CL2023

Improving BERT with Hybrid Pooling Network and Drop Mask

Qian Chen, Wen Wang, Qinglin Zhang +3

Transformer-based pre-trained language models, such as BERT, achieve great success in various natural language understanding tasks. Prior research found that BERT captures a rich h…

cs.CL2023

Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling

Linhan Zhang, Qian Chen, Wen Wang +4

Definition modeling is an important task in advanced natural language applications such as understanding and conversation. Since its introduction, it focus on generating one defini…

cs.CL2023

Meeting Action Item Detection with Regularized Context Modeling

Jiaqing Liu, Chong Deng, Qinglin Zhang +2

Meetings are increasingly important for collaborations. Action items in meeting transcripts are crucial for managing post-meeting to-do tasks, which usually are summarized laboriou…

cs.CL2023

Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)

Qinglin Zhang, Chong Deng, Jiaqing Liu +7

ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SL…