Summary of Prompting For Numerical Sequences: a Case Study on Market Comment Generation, by Masayuki Kawarada et al.
Prompting for Numerical Sequences: A Case Study on Market Comment Generation
by Masayuki Kawarada, Tatsuya Ishigaki, Hiroya Takamura
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 Large language models (LLMs) have been successfully applied to various data-to-text generation tasks, including tables, graphs, and time-series numerical data. However, there is a lack of research on prompting techniques for generating text from time-series numerical data. This study investigates different input representations, such as token sequences and structured formats like HTML, LaTeX, and Python-style code, for the task of Market Comment Generation. The results show that prompts resembling programming languages outperform those similar to natural languages or longer formats like HTML and LaTeX. These findings provide insights into creating effective prompts for tasks generating text from numerical sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how we can use computers to turn numbers into words. They tried different ways of telling the computer what to do, like writing code or using a natural language style. The goal was to generate comments about stock prices based on those numbers. Surprisingly, the results showed that using code-like prompts worked better than using natural language prompts. This study helps us understand how to make our computers create helpful text from numbers. |
Keywords
» Artificial intelligence » Prompting » Text generation » Time series » Token
