Summary of Mirai: Evaluating Llm Agents For Event Forecasting, by Chenchen Ye et al.
MIRAI: Evaluating LLM Agents for Event Forecasting
by Chenchen Ye, Ziniu Hu, Yihe Deng, Zijie Huang, Mingyu Derek Ma, Yanqiao Zhu, Wei Wang
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The abstract presents a novel benchmark called MIRAI designed to evaluate Large Language Model (LLM) agents’ forecasting capability and reliability in predicting international events. The authors introduce an agentic environment featuring an extensive database of historical events, textual news articles, and APIs for accessing different tools. The benchmark assesses LLM agents’ abilities from short-term to long-term forecasting, focusing on three dimensions: information integration, code-based tool-use, and joint reasoning over historical knowledge. The authors aim to establish a reliable framework for assessing the capabilities of LLM agents in forecasting international events, contributing to more accurate and trustworthy models for international relation analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new benchmark called MIRAI that helps evaluate how well large language models can predict future international events. These models are like super-smart assistants that can learn from lots of information and make predictions based on what they know. The authors created a special environment where these models can practice predicting different types of international events, like wars or agreements between countries. They also tested how well the models do at short-term and long-term forecasting. The goal is to create a reliable way to measure how good these models are at making predictions, which can help people make better decisions in the future. |
Keywords
» Artificial intelligence » Large language model
