1 citations · 2 across the 5 of their papers we have counts for
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Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings
Zifeng Cheng, Jie Zheng, Zhiwei Jiang +4
Large language models (LLMs) have shown strong potential as training-free text encoders for long-context embeddings. Existing approaches primarily improve information flow under ca…
RegionMarker: A Region-Triggered Semantic Watermarking Framework for Embedding-as-a-Service Copyright Protection
Shufan Yang, Zifeng Cheng, Zhiwei Jiang +5
Embedding-as-a-Service (EaaS) is an effective and convenient deployment solution for addressing various NLP tasks. Nevertheless, recent research has shown that EaaS is vulnerable t…
A Debiased Nearest Neighbors Framework for Multi-Label Text Classification
Zifeng Cheng, Zhiwei Jiang, Yafeng Yin +5
Multi-Label Text Classification (MLTC) is a practical yet challenging task that involves assigning multiple non-exclusive labels to each document. Previous studies primarily focus…
Multi-Prompting Decoder Helps Better Language Understanding
Zifeng Cheng, Zhaoling Chen, Zhiwei Jiang +4
Recent Pre-trained Language Models (PLMs) usually only provide users with the inference APIs, namely the emerging Model-as-a-Service (MaaS) setting. To adapt MaaS PLMs to downstrea…