Summary of Context Is Key: a Benchmark For Forecasting with Essential Textual Information, by Andrew Robert Williams et al.
Context is Key: A Benchmark for Forecasting with Essential Textual Information
by Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 tackles the challenge of improving forecasting in various domains by developing a novel benchmark that incorporates natural language context. The existing numerical data-driven approaches are insufficient, as they neglect crucial contextual information that human forecasters often rely on. To address this limitation, the authors introduce “Context is Key” (CiK), a time-series forecasting benchmark that pairs numerical data with textual context, requiring models to integrate both modalities. The CiK benchmark evaluates a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. The experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and reveal their critical shortcomings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes forecasting better by adding words to help us understand what’s going on. Right now, we use just numbers to make predictions, but that’s not enough. Human forecasters often look at other things too, like background information and rules. The authors created a special test called “Context is Key” (CiK) that gives models both numbers and words to work with. They tried different ways of doing this, including some new ideas using language models. The results show that adding context makes forecasting better, and it’s especially good when we use language models. |
Keywords
» Artificial intelligence » Time series
