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A Summary of 'Is Cosine-Similarity of Embeddings Really About Similarity?'
This is a summary of the AI research paper: Is Cosine-Similarity of Embeddings Really About Similarity?
Available at: https://arxiv.org/pdf/2403.05440v1.pdf
This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality.
As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries.
You can find the introductory section of this recording provided below...
This is a summary of "Is Cosine-Similarity of Embeddings Really About Similarity?" published on March 11, 2024, by Harald Steck and others from Netflix Inc. and Cornell University. In this paper, the authors examine the application and efficacy of cosine similarity as a measure for quantifying semantic similarity between high-dimensional objects within learned low-dimensional feature embeddings. Despite its popularity, the authors highlight observable inconsistencies in performance compared to unnormalized dot-products between embedding vectors. Through analytical exploration of embeddings derived from regularized linear models, the study demonstrates how cosine similarity can produce arbitrary and, in some models, non-unique similarity values. This is attributed to the degree of freedom in learned embeddings, exacerbated by different regularization practices in model training, which can inadvertently affect the resulting similarities when applying cosine similarity.
The analysis focuses on linear Matrix Factorization (MF) models to elucidate these abnormalities, deriving closed-form solutions that reveal how regularization choices influence cosine similarities. Notably, the paper discusses the potential for arbitrary results stemming from column rescaling in embeddings, illustrating how specific regularization approaches maintain invariance to these adjustments. Consequently, it's shown that cosine similarities can depend significantly on arbitrary diagonal matrices introduced during regularization, leading to potentially opaque and unintended outcomes in similarity measures.
The authors caution against blind reliance on cosine similarity for evaluating semantic similarities due to these inherent limitations and arbitrary influences. By dissecting the impact of regularization on cosine similarities and identifying the potential for arbitrary similarity scores, this paper casts a critical perspective on widely adopted practices in embedding analysis. The insights serve as a cautionary note for researchers and practitioners, prompting the consideration of alternative methods and more nuanced interpretations of similarity measurements in embeddings.
Available at: https://arxiv.org/pdf/2403.05440v1.pdf
This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality.
As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries.
You can find the introductory section of this recording provided below...
This is a summary of "Is Cosine-Similarity of Embeddings Really About Similarity?" published on March 11, 2024, by Harald Steck and others from Netflix Inc. and Cornell University. In this paper, the authors examine the application and efficacy of cosine similarity as a measure for quantifying semantic similarity between high-dimensional objects within learned low-dimensional feature embeddings. Despite its popularity, the authors highlight observable inconsistencies in performance compared to unnormalized dot-products between embedding vectors. Through analytical exploration of embeddings derived from regularized linear models, the study demonstrates how cosine similarity can produce arbitrary and, in some models, non-unique similarity values. This is attributed to the degree of freedom in learned embeddings, exacerbated by different regularization practices in model training, which can inadvertently affect the resulting similarities when applying cosine similarity.
The analysis focuses on linear Matrix Factorization (MF) models to elucidate these abnormalities, deriving closed-form solutions that reveal how regularization choices influence cosine similarities. Notably, the paper discusses the potential for arbitrary results stemming from column rescaling in embeddings, illustrating how specific regularization approaches maintain invariance to these adjustments. Consequently, it's shown that cosine similarities can depend significantly on arbitrary diagonal matrices introduced during regularization, leading to potentially opaque and unintended outcomes in similarity measures.
The authors caution against blind reliance on cosine similarity for evaluating semantic similarities due to these inherent limitations and arbitrary influences. By dissecting the impact of regularization on cosine similarities and identifying the potential for arbitrary similarity scores, this paper casts a critical perspective on widely adopted practices in embedding analysis. The insights serve as a cautionary note for researchers and practitioners, prompting the consideration of alternative methods and more nuanced interpretations of similarity measurements in embeddings.
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By James Bentley
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This is a summary of the AI research paper: Is Cosine-Similarity of Embeddings Really About Similarity?
Available at: https://arxiv.org/pdf/2403.05440v1.pdf
This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality.
As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries.
You can find the introductory section of this recording provided below...
This is a summary of "Is Cosine-Similarity of Embeddings Really About Similarity?" published on March 11, 2024, by Harald Steck and others from Netflix Inc. and Cornell University. In this paper, the authors examine the application and efficacy of cosine similarity as a measure for quantifying semantic similarity between high-dimensional objects within learned low-dimensional feature embeddings. Despite its popularity, the authors highlight observable inconsistencies in performance compared to unnormalized dot-products between embedding vectors. Through analytical exploration of embeddings derived from regularized linear models, the study demonstrates how cosine similarity can produce arbitrary and, in some models, non-unique similarity values. This is attributed to the degree of freedom in learned embeddings, exacerbated by different regularization practices in model training, which can inadvertently affect the resulting similarities when applying cosine similarity.
The analysis focuses on linear Matrix Factorization (MF) models to elucidate these abnormalities, deriving closed-form solutions that reveal how regularization choices influence cosine similarities. Notably, the paper discusses the potential for arbitrary results stemming from column rescaling in embeddings, illustrating how specific regularization approaches maintain invariance to these adjustments. Consequently, it's shown that cosine similarities can depend significantly on arbitrary diagonal matrices introduced during regularization, leading to potentially opaque and unintended outcomes in similarity measures.
The authors caution against blind reliance on cosine similarity for evaluating semantic similarities due to these inherent limitations and arbitrary influences. By dissecting the impact of regularization on cosine similarities and identifying the potential for arbitrary similarity scores, this paper casts a critical perspective on widely adopted practices in embedding analysis. The insights serve as a cautionary note for researchers and practitioners, prompting the consideration of alternative methods and more nuanced interpretations of similarity measurements in embeddings.
Available at: https://arxiv.org/pdf/2403.05440v1.pdf
This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality.
As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries.
You can find the introductory section of this recording provided below...
This is a summary of "Is Cosine-Similarity of Embeddings Really About Similarity?" published on March 11, 2024, by Harald Steck and others from Netflix Inc. and Cornell University. In this paper, the authors examine the application and efficacy of cosine similarity as a measure for quantifying semantic similarity between high-dimensional objects within learned low-dimensional feature embeddings. Despite its popularity, the authors highlight observable inconsistencies in performance compared to unnormalized dot-products between embedding vectors. Through analytical exploration of embeddings derived from regularized linear models, the study demonstrates how cosine similarity can produce arbitrary and, in some models, non-unique similarity values. This is attributed to the degree of freedom in learned embeddings, exacerbated by different regularization practices in model training, which can inadvertently affect the resulting similarities when applying cosine similarity.
The analysis focuses on linear Matrix Factorization (MF) models to elucidate these abnormalities, deriving closed-form solutions that reveal how regularization choices influence cosine similarities. Notably, the paper discusses the potential for arbitrary results stemming from column rescaling in embeddings, illustrating how specific regularization approaches maintain invariance to these adjustments. Consequently, it's shown that cosine similarities can depend significantly on arbitrary diagonal matrices introduced during regularization, leading to potentially opaque and unintended outcomes in similarity measures.
The authors caution against blind reliance on cosine similarity for evaluating semantic similarities due to these inherent limitations and arbitrary influences. By dissecting the impact of regularization on cosine similarities and identifying the potential for arbitrary similarity scores, this paper casts a critical perspective on widely adopted practices in embedding analysis. The insights serve as a cautionary note for researchers and practitioners, prompting the consideration of alternative methods and more nuanced interpretations of similarity measurements in embeddings.