Sentence embedding is a cornerstone in NLP. Whitening has been claimed to be an effective method to improve embeddings obtained from Large Language Models (LLMs) for sentence embedding. However, we find that the effectiveness of whitening is model-dependent and task-dependent. In particular, whitening degenerates embeddings for classification tasks. The conclusion is supported by extensive experiments. A by-product of our research is embedding evaluation platform for LLMs called SentEval+