Prompting strategies yield varied results across LLMs, study finds

A recent benchmark study compared six prompting strategies across Mistral-7B and GPT-4o-mini, revealing that the optimal approach varies significantly between models. Self-consistency, which involves voting on multiple generated answers, proved effective for Mistral-7B but underperformed a basic zero-shot prompt on GPT-4o-mini. Few-shot prompting, conversely, was detrimental to Mistral-7B but highly effective for GPT-4o-mini. The study also found that Tree-of-Thought prompting was costly and ineffective, while structured output via API-level enforcement was more reliable than prompt-based instructions. AI

IMPACT Highlights the need for model-specific prompt tuning, cautioning against universal generalizations.

RANK_REASON Benchmarking study of prompting techniques on LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• GPT-4o-mini
• GSM8K
• Mistral-7B
• Ollama
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