Why Telling an LLM to Be Careful Doesn't Work: Penalty Prompts Had a 0% Abstention Rate
Ten reproducible experiments on Gemma 2, 3 and 4 with Ollama. Code included. Every developer working with Large Language Models has tried some variation of this prompt: "Answer the question. If you are not completely sure, say 'I don't know'. Do not guess. Accuracy is critical." We write it in system prompts, chain it into agent workflows, and wrap it in bold markdown. We assume that if we make…
A recent study conducted on Gemma 2, 3, and 4 models using Ollama found that penalty prompts are ineffective in making language models admit ignorance or abstain from answering when they are not completely sure. In ten controlled experiments, models exhibited an abstention rate of 0%, consistently bluffing with near-total confidence and hallucinating information.
This phenomenon is attributed to the mathematical inevitability of Good-Turing missing mass, symmetric reinforcement learning rewards, and the greedy argmax bottleneck in autoregressive language models. The researchers concluded that prompt engineering is not an effective solution and instead recommend building physical, sub-15ms circuit breakers to prevent forced hallucinations in production agent architectures.
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