Beyond Semantic Search: A Hands-On Guide to Exploring Embedding Models

Why Look Beyond Semantic Search When Studying Embedding Models

Embedding models are nearly ubiquitous in today's AI applications — from retrieval-augmented generation (RAG) to recommendation systems, they play a central role. Yet most developers' understanding of embedding models stops at "turn text into vectors, then compute cosine similarity for semantic search."

A Reddit user recently posed a question to the community: beyond semantic search, what interesting experiments can help you truly understand embedding models? This cuts to the heart of the matter — embedding vectors are fundamentally a model's compressed representation of language, concepts, and even the world itself. The information encoded within them is far richer than just "finding similar text."

This article draws on that question to outline a series of hands-on experiments you can try yourself, helping you develop a deeper understanding of what these high-dimensional vectors actually capture.

Vector Arithmetic: Exploring the Directionality of Semantics

The most classic and intuitive experiment is verifying "vector arithmetic" within embedding space. From the word vector era, there's a famous example: king - man + woman ≈ queen. You can recreate similar experiments with modern sentence vector models to see whether they preserve composable semantic relationships in their spatial geometry.

Things to Try

• Compute the result vector of "Paris - France + Japan" and check whether it lands close to "Tokyo"
• Compare how different models (e.g., OpenAI's text-embedding, BGE, E5) perform on the same arithmetic tasks
• Test whether these relationships hold at the phrase and full-sentence level

These experiments offer an intuitive window into the geometric structure of embedding space — and reveal whether a model has genuinely learned transferable semantic directions, rather than just surface-level lexical matching.

Clustering and Visualization: Seeing the Structure of High-Dimensional Space

Embedding vectors are typically hundreds or even thousands of dimensions — far beyond human perception. By using dimensionality reduction techniques (such as t-SNE, UMAP, or PCA) to project them onto a 2D or 3D plane, you can visually observe how data clusters in space.

One valuable experiment: collect texts from different domains (news, code, poetry, legal documents), embed them, then apply clustering to see whether the model spontaneously groups similar content together. You'll find that different models vary significantly in their "sensitivity" to domain boundaries.

Going further, try reverse-analyzing cluster centers — extract representative samples from each cluster to understand whether the model organizes space by topic, style, or syntactic structure.

Anisotropy and Distribution Properties

One often-overlooked but highly valuable research direction is the "anisotropy" of embedding space. Research has shown that vectors produced by many pretrained models aren't uniformly distributed across the space — instead, they're crammed into a narrow cone-shaped region. This directly affects the reliability of similarity computations.

Suggested Experiments

• Randomly sample a large number of unrelated texts and compute the pairwise cosine similarity distribution. If many random texts show consistently high similarity (e.g., clustering above 0.6), the space exhibits significant anisotropy
• Compare similarity distributions before and after applying whitening or normalization
• Test how this phenomenon affects downstream retrieval quality

Understanding this helps explain why "seemingly unrelated texts" sometimes receive high similarity scores.

The root cause of anisotropy lies in the training objectives and architectural design of pretrained language models (such as the BERT family). During training, models tend to concentrate high-frequency words and general semantics along specific directions, causing output vectors to "collapse" toward the same region of high-dimensional space rather than spreading uniformly. Academics typically use "Effective Rank" to quantify this — an ideally isotropic embedding space would fully utilize all dimensions, while real models often activate only a fraction of them.

Common engineering mitigations include post-processing whitening (transforming the distribution to zero mean and unit covariance), as well as specialized approaches like BERT-flow and BERT-whitening. Notably, even modern embedding models optimized with instruction fine-tuning or contrastive learning (such as BGE and E5) haven't fully eliminated anisotropy — they've only reduced its severity. In high-precision retrieval scenarios, understanding the degree of anisotropy in your target model is a prerequisite that can't be ignored during model selection and tuning.

Cross-Lingual and Cross-Modal Alignment

Multilingual embedding models claim to map semantically equivalent text from different languages to nearby vector positions. You can verify this yourself: embed the same sentence in multiple languages and check whether the resulting vectors are sufficiently close in space.

This experiment not only tests a model's cross-lingual capabilities but can also expose its weaknesses on low-resource languages. Similarly, if you're using a multimodal embedding model (like CLIP), you can test whether images and their textual descriptions are truly aligned.

CLIP (Contrastive Language-Image Pretraining) was introduced by OpenAI in 2021. It uses contrastive learning to jointly train an image encoder and a text encoder, bringing images and textual descriptions of the same concept close together in a shared vector space. This alignment isn't perfect — the model excels on objects and scenes that appear frequently in its training data, but often fails on abstract concepts, culturally specific imagery, or fine-grained attributes (e.g., "the red cup on the left").

Multilingual embedding models (such as mBERT, LaBSE, and multilingual-E5) are trained on multilingual parallel corpora to map text from different languages into the same space. When experimenting, pay particular attention to symmetry between language pairs: Is the embedding distance from Chinese to English consistent with English to Chinese? The alignment quality gap between high-resource languages (Chinese, English, French) and low-resource languages (Swahili, Tamil) often reveals a stark performance cliff.

Robustness and Adversarial Probing

To truly understand an embedding model's behavior, robustness testing is essential:

• Perturbation testing: Make small changes to input text (add punctuation, change capitalization, introduce spelling errors) and observe the magnitude of vector changes. A stable model should be insensitive to meaningless perturbations
• Negation testing: How far apart in vector space are "I love this movie" and "I don't love this movie"? Many models handle negation quite poorly — this is a weakness worth digging into
• Length bias: Are short and long versions of the same content mapped to similar positions, or does text length itself dominate the vector?

These experiments help you identify a model's failure modes before deployment, preventing surprises in production.

Weak negation handling is a systemic flaw in embedding models. The root cause is that mainstream training objectives (such as masked language modeling and contrastive learning) don't explicitly require models to distinguish semantic opposites. In vector space, "like" and "don't like" share a great deal of lexical context, so models often encode them at very similar positions. This can cause serious errors in downstream tasks sensitive to negation, such as sentiment analysis and fact-checking.

For designing perturbation tests, you can draw inspiration from NLP robustness evaluation benchmarks (like CheckList and AdvGLUE), categorizing perturbations into three types: character-level (spelling errors, homophone substitution), lexical-level (synonym replacement, word order adjustment), and semantic-level (paraphrasing while preserving meaning). An engineeringly reliable model should show minimal vector change under character-level and lexical-level perturbations, high vector similarity under semantic-level paraphrasing, and a perceptible distance change when the meaning substantively changes (as with negation). Systematically recording how different models respond to these three perturbation categories is an efficient path to developing model selection intuition.

Practical Advice: From Hands-On Experiments to Engineering Intuition

The most effective way to understand embedding models is to treat them as a black box you can probe repeatedly. The recommended approach: start with a concrete dataset, fix one or two models, systematically run through the experiment types above, and document your results for cross-model comparison.

The real value of these experiments isn't arriving at textbook answers — it's building engineering intuition for vector space. When your RAG system retrieves strange results, or your recommendation system produces inexplicable suggestions, you'll be able to more quickly determine whether the issue stems from the model's inherent characteristics or from data and pipeline problems.

Embedding models are far more than just a tool for semantic search — they are the underlying representation through which modern AI systems understand the world. Investing time to explore their boundaries will pay dividends in every application you build.