More Samples or More Prompt Inputs? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering
Bingsheng YaoGuiming ChenRuishi ZouYuxuan LuJiachen LiShao ZhangSijia LiuJames HendlerDakuo Wang
Bingsheng YaoGuiming ChenRuishi Zou
While most existing works on LLM prompt-engineering focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can't we design and leverage multiple prompt inputs together to further improve the LLM performance? In this work, we propose In-Context Sampling (ICS), a low-resource LLM prompt-engineering technique to produce the most confident prediction results by optimizing the construction of multiple ICL prompt inputs. Extensive experiments with two SOTA LLMs (FlanT5-XL and Mistral-7B) on three NLI datasets (e-SNLI, Multi-NLI, and ANLI) illustrate that ICS can consistently enhance LLM's prediction performance and confidence. An ablation study suggests that a diversity-based ICS strategy may further improve LLM's performance, which sheds light on a new yet promising future research direction.