Summary of Prompt Mining For Language-based Human Mobility Forecasting, by Hao Xue et al.
Prompt Mining for Language-based Human Mobility Forecasting
by Hao Xue, Tianye Tang, Ali Payani, Flora D. Salim
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel framework proposes a prompt mining approach to language-based mobility forecasting, leveraging large language models. The paper introduces a two-stage process: first, generating prompts based on information entropy, and second, refining these prompts using mechanisms like the chain of thought. Experimental results demonstrate the superiority of generated prompts over fixed templates, highlighting the potential for improving language model forecasting performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language-based mobility forecasting uses large language models to predict human movement patterns. The challenge is creating effective prompts that transform numerical data into natural language sentences. This paper introduces a new approach called prompt mining, which generates and refines prompts using information entropy and mechanisms like chain of thought. The results show that generated prompts outperform fixed templates, offering a promising direction for improving forecasting accuracy. |
Keywords
» Artificial intelligence » Language model » Prompt
