Update README.md

README.md

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+license: apache-2.0
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 <p align="center"><h1>Raw Dataset from "Approaching Human-Level Forecasting with Language Models"</h1></p>
 
-<p align="center">This documentation provides an overview of the raw dataset utilized in our research paper, <strong><a href="https://arxiv.org/abs/2402.18563" target="_blank">Approaching Human-Level Forecasting with Language Models</a></strong>, authored by Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt.</p>
+<p>This documentation provides an overview of the raw dataset utilized in our research paper, <strong><a href="https://arxiv.org/abs/2402.18563" target="_blank">Approaching Human-Level Forecasting with Language Models</a></strong>, authored by Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt.</p>
 
 <h2>Data Source and Format</h2>
 <p>The dataset originates from forecasting platforms such as Metaculus, Good Judgment Open, INFER, Polymarket, and Manifold. These platforms engage users in predicting the likelihood of future events by assigning probabilities to various outcomes. The data structure encompasses:</p>
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 <h2>Research Significance</h2>
 <p>This dataset plays a crucial role in our study, enabling us to explore the capabilities of language models in forecasting and their potential to achieve human-level performance in predicting future events. By analyzing this vast array of user-generated forecasts, our research aims to shed light on the predictive power and limitations of current language modeling techniques.</p>
 
-<p>For more details on our methodology and findings, please refer to our paper linked at the beginning of this document.</p>
+<p>For more details on our methodology and findings, please refer to our paper linked at the beginning of this document.</p>