Does it matter which examples you choose for few-shot prompting?

The article discusses the importance of few-shot example selection in prompt engineering for large language models (LLMs), using the tool Libretto. The author conducted an experiment using Libretto's Experiments feature, testing different sets of few-shot examples on a task called Emoji Movie from the Big Bench benchmark. The results showed a significant difference in accuracy depending on the few-shot examples used, with one variation even performing worse than the baseline prompt with no examples.

The author concluded that few-shot example selection significantly impacts accuracy. They also highlighted that it's challenging to predict what a computer learns from examples, emphasizing the need for careful testing and variation in prompts. The author promotes Libretto as a tool to simplify this process, offering optimization of prompts at the press of a button.

Key takeaways

Few-shot example selection is crucial for the accuracy of large language models (LLMs).
Even one example can significantly improve the LLM's understanding of the task and its results.
Unexpected patterns in few-shot examples, such as all answers being one word long, can lead to inaccurate results.
Libretto is a tool that can help optimize and monitor LLM prompts, making the process of prompt engineering less tedious.

Does it matter which examples you choose for few-shot prompting? | Empirical Prompt Engineering

Key takeaways

Discussion (0)