Few-shot prompting effectiveness varies widely across LLMs, study finds

By PulseAugur Editorial · [1 sources] · 2026-07-25 00:31

A new study published on arXiv investigates the effectiveness of few-shot prompting across various large language models, examining how different shot counts impact classification performance. The research analyzed five models, including Gemini Flash Lite, GPT-4o mini, and several Llama variants, across six shot-count configurations on the AG News benchmark. Findings revealed distinct behavioral patterns, with some models showing gains, others suffering catastrophic zero-shot failure, and some degrading with more examples. The study also identified and corrected a parsing artifact that had previously skewed performance metrics for one model. AI

IMPACT Reveals that optimal prompting strategies are model-specific, not universal, impacting how developers fine-tune LLM performance.

RANK_REASON Academic paper detailing empirical study of LLM prompting techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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Few-shot prompting effectiveness varies widely across LLMs, study finds

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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ashvi Soni · 2026-07-25 00:31

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 c…

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When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness

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