Preprint Open access
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 …