Summary of Forecastbench: a Dynamic Benchmark Of Ai Forecasting Capabilities, by Ezra Karger et al.
ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities
by Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, Philip E. Tetlock
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 framework for evaluating the accuracy of machine learning (ML) systems on forecasting questions is introduced, with a dynamic benchmark called ForecastBench that automatically generates and updates 1,000 forecasting questions. The benchmark aims to eliminate data leakage by only including questions about future events with no known answer at the time of submission. The capabilities of current ML systems are quantified by collecting forecasts from expert (human) forecasters, the general public, and language models (LLMs) on a random subset of questions. While LLMs have achieved super-human performance on many benchmarks, they perform less well here, with expert forecasters outperforming the top-performing LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning systems can help make predictions about future events, but there isn’t a good way to measure how accurate these predictions are. A new tool called ForecastBench helps solve this problem by creating a large set of questions about things that will happen in the future, with no answers available yet. The creators tested different types of forecasters – human experts, regular people, and computer programs called language models – on some of these questions. They found that even the best language models were not as good at making predictions as human experts. |
Keywords
* Artificial intelligence * Machine learning
