Interpolating Embeddings: Nudging Sentences Toward One Another

Posted on: March 27th 2025

What Happens When You Slightly Nudge a Sentence Toward Another?

Can we watch a sentence gradually morph from one idea to another, like a linguistic fade from day to night? With embedding interpolation, we can! This technique allows us to explore the continuum between two different sentences by embedding them using LLMs and then decoding intermediate steps back to text. In case you want to follow along, here’s the notebook where you can compare your own results!

The results? Unexpected blends, subtle shifts in tone, and even entirely new semantic twists.
For example, what’s in-between:

• A calm sunrise over a serene lake.
• A bustling city street under neon lights.

Understanding Embedding Interpolation

Embedding interpolation is a fascinating area in machine learning where we blend embeddings—whether text or image—and attempt to decode them back into their respective spaces. While encoding into an embedding is relatively straightforward, decoding is inherently complex. This is because embeddings reside in a latent space, where each vector element corresponds to an abstract feature that only the model comprehends.

By calculating intermediate points (e.g., at 10%, 20%, etc.) between two embeddings and decoding them, we can observe how meaning transforms.

The Challenge of Latent Spaces

Latent spaces provide structured ways for models to understand relationships between data points, but they introduce unique challenges for interpolation:

• Continuity vs. Discreteness: While latent space is continuous, text is discrete, meaning interpolated embeddings may not always yield syntactically correct or semantically meaningful outputs.
• Non-One-to-One Mapping: There is no direct, reversible mapping between latent space and the original space. Decoding embeddings requires a compatible decoder or a stable diffusion model to extract semantics and reconstruct outputs.
• Model Specific Constraints: The encoding architecture affects how well embeddings can be interpolated. Normalization techniques and fine-tuned models (e.g., k-nearest neighbors with cosine similarity) can help but introduce trade-offs.

Challenges in Embedding Interpolation

1. Decoder Compatibility

Interpolated embeddings may sometimes fall outside the decoder’s expected range, leading to nonsensical outputs. Normalization and regularization techniques can help keep them within a meaningful boundary.

2. Text vs. Image Interpolation

Empirical observations suggest that image interpolation (img2img) often yields better results than text interpolation. Why?

• Language follows syntax and semantics, restricting valid word combinations.
• Images, defined by continuous pixel values, allow smoother transitions than words do.

Examples of Sentence Transitions

Here’s an example of a gradual semantic shift from a serene natural scene to an urban landscape.

Assume we have embedding functions and a decoder:

Sentence1 = “A calm sunrise over a serene lake.”

Sentence2 = “A bustling city street under neon lights.”

Results:

• 0% A serene sunrise over a lake over a lake. The light of a lake over a lake shady lake.
• 10% A serene sunrise over a lake over a lakeshore. The light of a lake calming the lake.
• 20% A serene sunrise over a lake over a lakeshore. Lights pulsating into the sky.
• 30% A serene lake over a lake with a lighthouse. The sunrise over a lake swanky in the morning.
• 40% A serene lake over a lake with a bright sunrise. Lights rushing over a bustling cityscape.
• 50% A peaceful street under a bright skyline. A bustling city bustling with neon lights.
• 60% A bustling city under a bright skyline. A bustling street light under a bustling neon cityscape.
• 70% A bustling city street under a bright neon light.
• 80% A bustling city street under a bright neon neon lighthouse. A bustling city under a bustling city lights.
• 90% A bustling city street under a bright neon neon lighthouse with bustling street lights.
• 100% A bustling city street under a neon neon lighthouse with bustling street lights, bustling taxis and bustling pedestrians.

Key Observations

• Steady Semantic Shift: The transition begins with serene imagery (“sunrise,” “lake”) and gradually morphs into an urban scene (“bustling city,” “neon”).
• Structural Artifacts: Early outputs contain repeated phrases (“over a lake over a lake”), indicating that the decoder clings to familiar patterns before new elements emerge.
• Abrupt Lexical Transformations: As certain tokens dominate the interpolation, words like “lighthouse” appear early, while “pedestrians” only emerge later, aligning closer to city scenes.

Not All Transitions Are Smooth

While some interpolations flow naturally, others exhibit abrupt shifts. Consider these examples:

Here are three examples:

Active vs. Passive Voice

1. The chef cooked the meal.
2. The chef cooked the meal.
3. The chef cooked the meal.
4. The chef cooked the meal.
5. The meal was cooked by the chef.
6. The meal was cooked by the chef.
7. The meal was cooked by the chef.
8. The meal was cooked by the chef.
9. The meal was cooked by the chef.
10. The meal was cooked by the chef.
11. The meal was cooked by the chef.

Different Tenses

1. I will go to the store.
2. I will go to the store.
3. I will go to the store.
4. I will go to the store.
5. I will go to the store.
6. I will go to the store.
7. I will go to the store.
8. I went to the store.
9. I went to the store.
10. I went to the store.
11. I went to the store.

Positive vs. Negative Sentiment

1. This is the best day of my life.
2. This is the best day of my life.
3. This is the best day of my life.
4. This is the best day of my life.
5. This is the best day of my life.
6. This is the worst day of my life.
7. This is the worst day of my life.
8. This is the worst day of my life.
9. This is the worst day of my life.
10. This is the worst day of my life.
11. This is the worst day of my life.

In each case, there is an abrupt shift from the first to the second phrase.

Why do these transitions fail?

• Languages are discrete: The decoder snaps to one phrase or another instead of a gradual morph.
• Opposites have similar embeddings: “Best day” and “worst day” are often closer in latent space than “normal day,” leading to abrupt shifts.
• Negative sentiments and passive voice dominate: The model’s embedding space may have more variety for negative sentiments, passive voice, and future tense, making transitions unpredictable.

Theoretical Insights

Several hypotheses may explain the behavior of embedding interpolation:

1. Positional Interference

Linear interpolation between word positions may introduce interference against semantic coherence, making gradual transitions difficult.

2. Phrase-Level vs. Word-Level Embeddings

Embedding entire phrases instead of individual words could improve interpolation, capturing broader contextual semantics rather than isolated word transitions.

3. Proper Noun Dominance

The persistence of proper nouns suggests that embeddings assign higher weights to them, possibly because they lack synonyms and are more distinct in training data.

Conclusion

Embedding interpolation unlocks new possibilities in understanding how models interpret language. While some transitions create smooth semantic blends, others remain abrupt due to the text’s discrete nature. At Straive, we believe refining decoder architectures, normalization techniques, and phrase-level embeddings can lead to more fluid and interpretable transformations in latent space.

Have you experimented with embedding interpolation? What surprising results have you found? Let’s discuss in the comments!

About the Author

Aakash Gorla

Aakash Gorla is a technology enthusiast with a deep interest in large language models and their underlying architectures. He enjoys exploring optimization techniques, model efficiency, and the evolving landscape of AI-driven systems. His interests lie at the intersection of deep learning, high-performance computing, and scalable software design, constantly seeking ways to enhance model performance and real-world applicability.