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Introduces PoolBench, a benchmark that isolates pooling strategies in decoder-only LLM concept representation, evaluating 19 strategies across 17 concepts and 3 models to provide a standardized protocol for comparing pooling choices.
This paper investigates principles of concept representation in sentence encoders through the lens of compositional semantics, identifying four key principles: fine-tuning recalibrates latent geometry, semantic signal concentrates in the final layer, hard negatives improve discrimination but not ranking, and supervision effectiveness depends on composition type.