MIRAI: Evaluating LLM Agents for Event Forecasting
Authors: name: Chenchen Ye, name: Ziniu Hu, name: Yihe Deng, name: Zijie Huang, name: Mingyu Derek Ma, name: Yanqiao Zhu, name: Wei Wang

Recent advancements in Large Language Models (LLMs) have empowered LLM agents
to autonomously collect world information, over which to conduct reasoning to
solve complex problems. Given this capability, increasing interests have been
put into employing LLM agents for predicting international events, which can
influence decision-making and shape policy development on an international
scale. Despite such a growing interest, there is a lack of a rigorous benchmark
of LLM agents' forecasting capability and reliability. To address this gap, we
introduce MIRAI, a novel benchmark designed to systematically evaluate LLM
agents as temporal forecasters in the context of international events. Our
benchmark features an agentic environment with tools for accessing an extensive
database of historical, structured events and textual news articles. We refine
the GDELT event database with careful cleaning and parsing to curate a series
of relational prediction tasks with varying forecasting horizons, assessing LLM
agents' abilities from short-term to long-term forecasting. We further
implement APIs to enable LLM agents to utilize different tools via a code-based
interface. In summary, MIRAI comprehensively evaluates the agents' capabilities
in three dimensions: 1) autonomously source and integrate critical information
from large global databases; 2) write codes using domain-specific APIs and
libraries for tool-use; and 3) jointly reason over historical knowledge from
diverse formats and time to accurately predict future events. Through
comprehensive benchmarking, we aim to establish a reliable framework for
assessing the capabilities of LLM agents in forecasting international events,
thereby contributing to the development of more accurate and trustworthy models
for international relation analysis.