Position Engineering: Boosting Large Language Models through Positional Information Manipulation
This addresses the problem of inefficient prompt modification for LLM users, offering a more efficient alternative to prompt engineering, though it appears incremental as it builds on existing prompt strategies.
The paper tackles the challenge of improving large language model (LLM) performance by introducing position engineering, a technique that manipulates positional information in prompts without altering text, and shows it substantially boosts results in retrieval-augmented generation and in-context learning scenarios.
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 involves altering the positional information in the prompt without modifying the text itself. We have evaluated position engineering in two widely-used LLM scenarios: retrieval-augmented generation (RAG) and in-context learning (ICL). Our findings show that position engineering substantially improves upon the baseline in both cases. Position engineering thus represents a promising new strategy for exploiting the capabilities of large language models.