Source URL: https://p.migdal.pl/blog/2025/01/dont-use-cosine-similarity/
Source: Hacker News
Title: Don’t use cosine similarity carelessly
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text explores the complexities and limitations of using cosine similarity in AI, particularly in the context of vector embeddings derived from language models. It critiques the blind application of cosine similarity to assess relationships between vectors, emphasizing that a nuanced understanding is essential for effective AI outcomes in data science and machine learning applications.
Detailed Description:
The provided text presents a detailed examination of cosine similarity, primarily focusing on its application and pitfalls within vector embeddings generated by machine learning models, especially Large Language Models (LLMs). It emphasizes the necessity of intentionality in the use of similarity measures when working with vectors and highlights several key points regarding the relationship between semantic meaning and mathematical similarity metrics.
– **Limitations of Cosine Similarity**:
– Cosine similarity can lead to superficial assessments of similarity, often matching questions to questions instead of questions to answers.
– Relying too heavily on cosine similarity may create false positives, where irrelevant items are deemed similar based on misleading patterns.
– **Understanding Vector Embeddings**:
– Embeddings map raw IDs to meaningful relationships between entities (e.g., “brother” and “sister”), which enhances machine learning processes.
– Vectors from LLMs can accurately capture the essence of text with minimal fine-tuning, indicating their potency in processing complex linguistic constructs.
– **Practical Implications**:
– The author calls attention to the fact that while cosine similarity is easy to compute, it serves as a “duct tape” solution that may obscure deeper analytical challenges.
– It suggests alternative methods for vector comparison that might be more suited to specific applications, such as using task-specific embeddings or fine-tuning models.
– **Challenges in High-Dimensional Spaces**:
– The narrative points out that traditional geometrical intuitions break down in high-dimensional vector spaces, complicating the proper evaluation of similarity.
– The risks associated with incorrectly trusting cosine similarity are particularly pronounced in real-world applications where context and meaning are paramount.
– **Broader Perspectives on Similarity**:
– The author highlights that similarity measures can be subjective and contextual, depending on the lens through which they are viewed (e.g., literary versus technical definitions of similarity).
– **Recommended Best Practices**:
– Custom training of embeddings on relevant datasets can yield better outcomes in similarity assessments.
– The author recommends crafting specific prompts and cleaning text prior to embedding, enabling more focused similarity evaluations.
This analysis underscores the importance for security and compliance professionals—especially those working with AI and machine learning—to carefully consider how similarity metrics are employed within their data pipelines. The insights shared could help in refining the methodology around AI models, thereby improving accuracy, relevance, and compliance with regulated data practices.