Summary of Approaching Human-level Forecasting with Language Models, by Danny Halawi et al.
Approaching Human-Level Forecasting with Language Models
by Danny Halawi, Fred Zhang, Chen Yueh-Han, Jacob Steinhardt
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 The paper explores whether language models (LMs) can accurately predict future events, competing with human forecasters. To achieve this, a retrieval-augmented LM system is developed, capable of searching for relevant information, generating forecasts, and aggregating predictions. A large dataset of questions from competitive forecasting platforms is collected to facilitate the study. The system’s end-to-end performance is evaluated against the aggregated human forecasts on a test set published after the knowledge cut-offs of the LMs. The results show that the system approaches the crowd aggregate of competitive forecasters, and in some cases, surpasses it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at whether computers can predict the future as well as humans do. They create a special computer program called a language model that can find information, make predictions, and combine them. To test this, they collect lots of questions from websites where people compete to predict what will happen next. The results show that the computer program does almost as well as humans do in making predictions. This is exciting because it could help make important decisions. |
Keywords
* Artificial intelligence * Language model
