Summary of Position Engineering: Boosting Large Language Models Through Positional Information Manipulation, by Zhiyuan He et al.
Position Engineering: Boosting Large Language Models through Positional Information Manipulation
by Zhiyuan He, Huiqiang Jiang, Zilong Wang, Yuqing Yang, Luna Qiu, Lili Qiu
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel technique called position engineering, which enhances the performance of large language models (LLMs) by modifying positional information in prompts without altering the text itself. The authors demonstrate that this approach outperforms traditional prompt engineering methods in two scenarios: retrieval-augmented generation (RAG) and in-context learning (ICL). The findings show that position engineering improves upon the baseline in both cases, making it a promising strategy for leveraging LLM capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make large language models work better. Right now, people are trying lots of different ways to get the best out of these models. One new idea is called position engineering. Instead of changing what you say to the model, you just change where you put it. This makes a big difference! The researchers tested this idea in two situations and found that it worked really well. It could be a game-changer for using language models. |
Keywords
» Artificial intelligence » Prompt » Rag » Retrieval augmented generation
