New Tool Reveals Unexpected LLM Performance Fluctuations with Few-Shot Learning
research#llm📝 Blog|Analyzed: Feb 28, 2026 17:45•
Published: Feb 28, 2026 12:29
•1 min read
•Zenn GeminiAnalysis
A new open-source tool, AdaptGauge, is making waves by revealing surprising performance dips in Large Language Models (LLMs) when using few-shot prompting. This research highlights the complex relationship between the number of examples provided and model accuracy, offering valuable insights for Prompt Engineering.
Key Takeaways
- •AdaptGauge is an open-source tool for evaluating LLM performance with varying numbers of examples.
- •The research found that LLM performance can sometimes decrease as the number of examples in few-shot prompting increases.
- •The study tested several Cloud and Local LLMs on tasks like classification and code correction.
Reference / Citation
View Original"It has been said that increasing the number of examples in a prompt improves the accuracy of the answer. However, when actually measured, there were cases where the performance decreased by increasing the examples."