Why does the stochastic nature of LLMs make explainability difficult?

LLMs are not deterministic systems; they generate responses based on a statistical representation of language rather than predictable, reproducible rules. This stochasticity makes it difficult to relate inputs to outputs in a predictable way and to explain exactly why specific content was generated, because there is no clear, deterministic pathway from prompt to response.

The evidence explains that, unlike deterministic systems where outputs are predictable and reproducible, LLMs produce responses from a statistical representation of language. Because the model is stochastic, it is hard to predictably connect inputs to outputs and to explain the generated content. The absence of a clear mechanistic pathway complicates efforts to justify LLM output, which in turn undermines transparency and accountability. Additionally, proprietary LLMs do not disclose training datasets, methods, fine-tuning, or inference procedures, further reducing visibility into how outputs arise.