More Samples Or More Prompts? Exploring Effective In-context Sampling For LLM Few-shot Prompt Engineering

Yao Bingsheng, Chen Guiming, Zou Ruishi, Lu Yuxuan, Li Jiachen, Zhang Shao, Sang Yisi, Liu Sijia, Hendler James, Wang Dakuo. Arxiv 2023

[Paper]    
Few Shot Prompting RAG

While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM’s performance? In this work, we propose In-Context Sampling (ICS), a low-resource LLM prompting technique to produce confident predictions by optimizing the construction of multiple ICL prompt inputs. Extensive experiments with three open-source LLMs (FlanT5-XL, Mistral-7B, and Mixtral-8x7B) on four NLI datasets (e-SNLI, Multi-NLI, ANLI, and Contract-NLI) and one QA dataset (CommonsenseQA) illustrate that ICS can consistently enhance LLMs’ performance. An in-depth evaluation with three data similarity-based ICS strategies suggests that these strategies can further elevate LLM’s performance, which sheds light on a new yet promising future research direction.

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