Summary of A Picture Is Worth a Thousand Numbers: Enabling Llms Reason About Time Series Via Visualization, by Haoxin Liu et al.
A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization
by Haoxin Liu, Chenghao Liu, B. Aditya Prakash
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 proposed TimerBed testbed evaluates the time-series reasoning (TsR) abilities of large language models (LLMs), which have shown reasoning capabilities across multiple domains. The testbed includes stratified reasoning patterns with real-world tasks, various supervised models as comparison anchors, and combines LLMs with reasoning strategies. Experimental results demonstrate that VL-Time, a prompt-based solution using visualization-modeled data and language-guided reasoning, enables multimodal LLMs to achieve non-trivial zero-shot (ZST) and powerful few-shot in-context learning (ICL) for time-series tasks, with an average performance improvement of 140% and token cost reduction of 99%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can reason about many things, but they haven’t been tested much on time series data. This paper creates a special testbed to see how well these models do at this task. The testbed has different patterns for reasoning about time series data and compares the results to some other methods. Surprisingly, the language models don’t do very well without any training or with only a little bit of training. But by using visualizations and giving them more guidance, the models can learn to reason about time series data much better. |
Keywords
» Artificial intelligence » Few shot » Prompt » Supervised » Time series » Token » Zero shot
