Evaluating the Impact of Temperature and Instruction Strategies on Hallucination in Large Language Models

Large Language Models (LLMs) have demonstrated impressive generative and reasoning abilities, yet their tendency to produce factually incorrect or fabricated information—so-called hallucinations—remains a key limitation. This study systematically examines how temperature and system instruction strategies affect hallucination behavior in open-source LLMs executed through the Ollama framework. Three representative models—Gemma 2B, Mistral 7B Instruct, and Phi-3 Mini—were evaluated on the TruthfulQA benchmark using zero-shot, few-shot, and “say-I-don’t-know” prompting paradigms. Performance was measured through exact match, token-level F1, semantic similarity, and embedding-based similarity metrics. Two-way ANOVA and3 Tukey post-hoc analyses revealed that system instruction significantly influenced factual accuracy across all models, while temperature effects were comparatively minor. Few-shot prompting achieved the highest mean F1 score (0.1889), indicating that example conditioning effectively constrained hallucinations. Conversely, “say-I-don’t-know” prompts increased semantic alignment but reduced precision, suggesting a conservative refusal bias. Embedding-based similarity analyses confirmed higher semantic consistency for zero-shot responses. The results highlight that prompt design exerts a stronger and more interpretable influence on hallucination than sampling stochasticity, offering practical guidance for improving the factual reliability of open-source LLMs.