When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness
Few-shot prompting, the practice of prepending a small number of input-output demonstration pairs to a query before presenting it to a large language model (LLM), is among the most widely adopted inference-time techniques in NLP. Yet little systematic work investigates how shot count interacts with model scale, architecture, and output format compliance in determining classification performance. This paper presents a controlled study of five LLMs across six shot-count configurations (k in {0,1,2,3,5,8}) on the AG News four-class benchmark (n=200). Our models span proprietary and open-source families: Gemini Flash Lite, GPT-4o-mini, Llama 3.1 8B, Llama 3.3 70B, and Llama 4 Scout 17B. We report macro-averaged F1 with 95% bootstrap confidence intervals (B=10,000), permutation-test p-values, and Cohen's d effect sizes across all 30 configurations. Our findings reveal four qualitatively distinct behavioral regimes: (1) models already well-calibrated at zero-shot that show modest, statistically insignificant gains (Gemini, GPT-4o-mini); (2) models that undergo catastrophic zero-shot failure but recover dramatically with a single example (Llama 3.1 8B, d=10.98, p<0.0001); (3) models optimal at zero-shot that degrade monotonically with additional examples (Llama 4 Scout); and (4) models exhibiting a U-shaped curve (Llama 3.3 70B: 0-shot F1=0.907, 2-shot F1=0.635, 5-shot F1=0.785 with parser corrected). We additionally identify, diagnose, and correct a systematic parsing artifact that artificially deflated Llama 3.3 70B performance by up to 206%, constituting a methodological contribution to LLM evaluation practice. Our results demonstrate that the relationship between shot count and classification performance is not monotonic, not universal, and not predictable from model scale alone.