On the contrary, the reason why A “small” language model—by which we generally mean one with on the order of millions (rather than billions) of parameters—struggles to mimic human conversation convincingly for several interlocking reasons:

Fewer parameters mean the model can store and manipulate far less information about language patterns, world knowledge, and subtle linguistic nuances.

As a result, it often resorts to simplistic or repetitive responses, rather than the rich variety of expression a human would use.

Small models tend to overfit to the specific data they were trained on, so they struggle with novel topics or unexpected turns in conversation.

They lack the depth to maintain a consistent persona, long-term context, or coherent thread over multiple turns, making their dialogue feel disjointed or “robotic.”

Passing a Turing test usually requires not just fluent language but also common-sense reasoning, up‑to‑date facts, and the ability to draw inferences.

With constrained capacity, small models cannot internalize large-scale factual databases or sophisticated reasoning patterns; they often hallucinate or give incorrect answers when pressed.

At their core, small LMs are powerful pattern‑matchers but lack the deeper latent structures (e.g., causal models, theory of mind) that larger models can approximate.

This leads to responses that may look grammatically correct but fail to capture intentions, emotions, or the pragmatic subtleties of human dialogue.

Implications for the Turing Test

Alan Turing’s original proposal envisioned an interlocutor capable of sustained, varied, and contextually appropriate conversation. Small language models simply don’t have the “brain‑like” resources—be it memory, breadth of knowledge, or reasoning scaffolds—to convincingly impersonate a human over an extended exchange. In short, they lack both the scale and depth required to fool a well‑informed judge.