Position Engineering: Boosting Large Language Models through Positional Information Manipulation

Zhiyuan He, Huiqiang Jiang, Zilong Wang, Yuqing Yang, Luna K. Qiu, Lili Qiu


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 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.
Anthology ID:
2024.emnlp-main.417
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7333–7345
Language:
URL:
https://aclanthology.org/2024.emnlp-main.417
DOI:
10.18653/v1/2024.emnlp-main.417
Bibkey:
Cite (ACL):
Zhiyuan He, Huiqiang Jiang, Zilong Wang, Yuqing Yang, Luna K. Qiu, and Lili Qiu. 2024. Position Engineering: Boosting Large Language Models through Positional Information Manipulation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7333–7345, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Position Engineering: Boosting Large Language Models through Positional Information Manipulation (He et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-main.417.pdf