The paradox of LLM behavior inside its own context window.

Understanding the Paradox of Large Language Model Behavior Within Its Context Window

In recent discussions about large language models (LLMs), a common point of confusion is how these models utilize their context window and why their behavior sometimes appears unpredictable or counterintuitive. To clarify, let’s explore the underlying mechanics of how LLMs process information within their context and the implications for user interactions.

The Nature of the Context Window

Unlike a human memory system that weights certain memories as more significant, an LLM’s context window functions differently. All tokens (words, phrases, or outputs) within this window are accessible to the model, but not with equal importance. The model does not possess an inherent understanding of which parts of the conversation or input are authoritative or most relevant. Instead, it treats everything as a continuous stream of text—a “giant soup”—and predicts the next token based purely on statistical likelihood derived from training data.

Predictive Processing Does Not Equate to Memory

It’s crucial to recognize that an LLM’s generation process is not akin to truly “remembering” previous statements in a human sense. When the model generates responses, it doesn’t recall earlier outputs or user directives as stored facts; it re-processes the entire visible text each time. Its goal is to produce the most statistically probable continuation, not necessarily to maintain fidelity to prior instructions or outputs.

Why Does This Lead to Apparent Contradictions?

One perplexing issue is that the same list or instruction that you supply to the model can be overlooked or deprioritized, especially if the surrounding prompt encourages further generation. For example, if you provide a list of tags and then ask for advice, the model might—even if the list remains in the context—prioritize generating “more” or “refining” rather than preserving the list exactly. This behavior stems from the model’s pattern recognition: it interprets your prompt as a signal to continue a pattern it has seen during training, such as “expand” or “refine,” rather than to simply repeat.

This is why the perceived behavior sometimes seems like “gaslighting”: the model is not intentionally ignoring your instructions but is following the statistically dominant patterns it learned, which can sometimes conflict with your explicit cues.

Practical Strategies to Achieve More Controlled Outputs

To mitigate this issue, practitioners often employ “anchoring” techniques that instruct the model to treat certain input as unchangeable. For instance:

• Explicit instructions such as: “Repeat this back exactly,