Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings
Large language models (LLMs) have shown strong potential as training-free text encoders for long-context embeddings. Existing approaches primarily improve information flow under causal attention and typically construct embeddings by uniformly averaging all token representations. However, for long documents, such mean p...
Zi-Feng Cheng, Jie Zheng, Zhiwei Jiang et al.
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