Summary of Whitening Not Recommended For Classification Tasks in Llms, by Ali Forooghi et al.
Whitening Not Recommended for Classification Tasks in LLMs
by Ali Forooghi, Shaghayegh Sadeghi, Jianguo Lu
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the effectiveness of whitening techniques in improving sentence embeddings obtained from Large Language Models (LLMs). While previous studies have claimed that whitening improves embedding quality, the authors find that its efficacy depends on both the model and task. In particular, they show that whitening actually degrades embeddings for classification tasks. The study’s conclusions are supported by extensive experiments using various whitening operations, including PCA, ZCA, PCA-Cor, ZCA-Cor, and Cholesky whitenings. Additionally, the authors introduce an embedding evaluation platform called SentEval+. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make language models better at understanding sentences. It’s like trying to get a clear picture of what words mean. Some people thought that “whitening” was a good way to do this, but it actually works differently depending on the model and what you’re using it for. They found that if you use whitening for things like classifying text, it might even make things worse! To figure out how well different models are working, they tried lots of different ways to “whiten” them. And they also made a special tool called SentEval+ to help test language models. |
Keywords
* Artificial intelligence * Classification * Embedding * Pca
