Whitening Not Recommended for Classification Tasks in LLMs
Sentence embedding is a cornerstone in NLP. Whitening has been claimed to be an effective operation to improve embedding quality obtained from Large Language Models (LLMs). However, we find that the efficacy of whitening is model-dependent and task-dependent. In particular, whitening degenerates embeddings for classification tasks. The conclusion is supported by extensive experiments. We also explored a variety of whitening operations, including PCA, ZCA, PCA-Cor, ZCA-Cor and Cholesky whitenings. A by-product of our research is embedding evaluation platform for LLMs called SentEval+.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationSentenceSentence EmbeddingSentence-EmbeddingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Switchable Whitening for Deep Representation Learning
Normalization methods are essential components in convolutional neural networks (CNNs). They either standardize or whiten data using statistics estimated in predefined sets of pixels. Unlike existing works that design no…
Domain Adaptationimage-classificationImage ClassificationRepresentation Learning+3Stochastic Whitening Batch Normalization
Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The re…
image-classificationImage ClassificationImproving STDP-based Visual Feature Learning with Whitening
In recent years, spiking neural networks (SNNs) emerge as an alternative to deep neural networks (DNNs). SNNs present a higher computational efficiency using low-power neuromorphic hardware and require less labeled data …
Computational EfficiencyGeneral Classificationimage-classificationImage ClassificationCan Large Language Models Assess Serendipity in Recommender Systems?
Serendipity-oriented recommender systems aim to counteract over-specialization in user preferences. However, evaluating a user's serendipitous response towards a recommended item can be challenging because of its emotion…
Binary ClassificationRecommendation SystemsImproving Generalization of Batch Whitening by Convolutional Unit Optimization
Batch Whitening is a technique that accelerates and stabilizes training by transforming input features to have a zero mean (Centering) and a unit variance (Scaling), and by removing linear correlation between channels (D…
image-classificationImage Classification