Inference As Interference: How LLMs Collide Semantic Waves To Create Meaning

Large Language Models (LLMs) like GPT-4 and their successors are at the forefront of modern artificial intelligence, driving advanced text generation, summarization, and even reasoning. But how do these models actually produce meaning from the seemingly chaotic collection of words, phrases, and contexts in data? An emerging perspective frames this process as “inference as interference”—where the model creates meaning by colliding semantic waves, much like waves in physics collide to create new patterns. In this post, we’ll break down what this analogy means, how LLMs really work behind the scenes, and why it matters for understanding both their power and their limits.

The Fundamentals: What Are Semantic Waves?

To unpack this metaphor, let’s start with what semantics refers to: the study of meaning in language. When you read a sentence, you don’t just process each word independently; your mind connects and interprets them together, creating a rich and layered sense of meaning.

Now, imagine each possible interpretation of a phrase as a “wave”—with amplitude (confidence), frequency (how commonly the meaning appears), and phase (its alignment with the context). As new information (words, context, or instructions) is introduced, these “semantic waves” interact, intensify, or cancel each other out, shaping the sentence’s emergent meaning.

How LLMs Use Interference for Inference

LLMs are built on transformer architectures, which use vast neural networks trained on terabytes of text. Under the hood, each word or token is mapped to a point in a high-dimensional space—an embedding. These embeddings hold not only the word’s dictionary meaning but its myriad shades of meaning in context, as learned from the model’s exposure to vast corpora.

As the LLM reads a prompt, it doesn’t process one word at a time in isolation. Rather, it uses attention mechanisms to weigh how much each prior word influences the prediction of the next word. Technically, these influences can be seen as waveforms—peaks and troughs of potential meanings—which overlap, reinforce, or cancel out as more context is fed into the model.

In other words, meaning “emerges” from the interference pattern of all these semantic waves coming together. Some interpretations gain strength (constructive interference), while others weaken or disappear (destructive interference), helping the model zero in on the most contextually appropriate output.

Example: “Bank” in Context

Take the polysemous word “bank.” Without context, it could mean the side of a river or a financial institution. If you feed the model:
“I sat down on the bank and watched the water flow past.”
The semantic waves for “river bank” and “financial bank” are both present initially, but as the rest of the sentence is processed, the wave for “river bank” gains strength while the other fades, due to interference with the contextual cues “sat down” and “water flow.”

This mirrors how our brains resolve ambiguity, but in LLMs, it’s a mathematical interference pattern—one calculated via matrix multiplications, dot products, and nonlinear functions.

Why This Matters: Power and Limitations

Understanding inference as interference has big implications:

Interpretability: It explains why LLMs can sometimes “hallucinate” facts—they’re not pulling from a knowledge database but generating the most likely next wave peak based on complex interference, which sometimes leads to plausible but untrue statements. See discussions on LLM hallucinations for more.
Creativity: The same process allows surprising or novel outputs, as rare but possible semantic waves occasionally constructively interfere to produce unexpected but valid language innovation. This is why LLMs can produce poetry, jokes, or even new metaphors.
Bias and Context Sensitivity: Since interference depends on training data and context, LLMs may reflect or even amplify biases. For a deep dive, check out this Harvard Data Science Review article on LLM bias.

Steps: Semantic Interference in Action

Encoding: The model converts input words into embeddings, representing their meanings numerically in context.
Attention & Weighting: Each word’s embedding is recalibrated by how much it attends to every other word—much like how the resonance of waves may amplify or diminish based on their alignment.
Aggregation & Interference: The model aggregates these signals, and the interference (when meanings overlap or contradict) determines which interpretations rise to the surface.
Decoding: Finally, the model decodes this high-level interference pattern back into a word or phrase, generating its output.

Practical Applications

This paradigm shift helps inform better prompt engineering, debugging LLM errors, and understanding their human-like creativity and flaws. For researchers and practitioners, it also suggests new ways to build more robust, interpretable models by tuning the way semantic interference is measured and managed.