Papers by Penghui Yang

3 papers
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022.naacl-industry)

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Challenge: a paper focuses on automatically generating the text of an ad to capture user interest for achieving higher click-through rate.
Approach: They propose a CTR-driven advertising text generation approach to generate ad texts based on user reviews.
Outcome: The proposed approach outperforms existing approaches on industrial datasets and on large-scale unpaired reviews.
LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)

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Challenge: Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs.
Approach: They propose a model that uses a constant-sized key-value cache to train long-context models.
Outcome: Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks.
MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)

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Challenge: telemedicine is a medical practice that provides patient care remotely using video conferencing tools.
Approach: They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance .
Outcome: The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues.

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