More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering

• URL: http://arxiv.org/abs/2311.09782v2
• Date: Tue, 2 Apr 2024 17:16:40 GMT
• Title: More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering
• Authors: Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang,
• Abstract summary: We propose In-Context Sampling (ICS) to produce confident predictions by optimizing the construction of multiple ICL prompt inputs. An in-depth evaluation with three data similarity-based ICS strategies suggests that these strategies can further elevate LLM's performance.
• Score: 35.086135550672864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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