Position Engineering: Boosting Large Language Models through Positional Information Manipulation

arXiv:2404.11216v1 Announce Type: new
Abstract: The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineering strategies aimed at modifying the prompt text to enhance task performance. In this paper, we introduce a novel technique termed position engineering, which offers a more efficient way to guide large language models. Unlike prompt engineering, which requires substantial effort to modify the text provided to LLMs, position engineering merely …

abstract arxiv boosting cs.ai cs.cl cs.lg engineering information language language models large language large language models llms manipulation novel paper performance prompt prompts quality researchers strategies text the prompt through type