  Semantic Textual Similarity (STS) constitutes a critical research direction
in computational linguistics and serves as a key indicator of the encoding
capabilities of embedding models. Driven by advances in pre-trained language
models and contrastive learning techniques, leading sentence representation
methods can already achieved average Spearman's correlation scores of
approximately 86 across seven STS benchmarks in SentEval. However, further
improvements have become increasingly marginal, with no existing method
attaining an average score higher than 87 on these tasks. This paper conducts
an in-depth analysis of this phenomenon and concludes that the upper limit for
Spearman's correlation scores using contrastive learning is 87.5. To transcend
this ceiling, we propose an innovative approach termed Pcc-tuning, which
employs Pearson's correlation coefficient as a loss function to refine model
performance beyond contrastive learning. Experimental results demonstrate that
Pcc-tuning markedly surpasses previous state-of-the-art strategies, raising the
Spearman's correlation score to above 90.
