Summary of Can Language Models Use Forecasting Strategies?, by Sarah Pratt et al.
Can Language Models Use Forecasting Strategies?
by Sarah Pratt, Seth Blumberg, Pietro Kreitlon Carolino, Meredith Ringel Morris
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel dataset of real-world events and associated human predictions is used to benchmark the forecasting ability of large language models (LLMs) in this study. The authors design several LLM-based forecasting architectures and evaluate their performance on the provided dataset using a custom evaluation metric. While LLMs show promise, they still struggle to make accurate predictions about future outcomes, with a tendency to overestimate the uncertainty of events. The findings suggest that developing a systematic approach to studying LLM forecasting is crucial for improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting really good at doing things like recognizing pictures and solving math problems. But what they’re not great at yet is predicting what will happen in the future. In this study, researchers created a special dataset of real events with people’s predictions about what would happen next. They then tested different types of large language models on this task and found that while they can make some good guesses, they still struggle to get it right most of the time. |
