MIRAI: Evaluating LLM Agents for Event Forecasting

TL;DR

MIRAI introduces a comprehensive benchmark to evaluate large language model agents' ability to predict international events across various time horizons, addressing the lack of systematic assessment tools in this domain.

Contribution

This paper presents MIRAI, a novel benchmark that systematically evaluates LLM agents' forecasting capabilities using a curated event database and tool integration, filling a critical gap in the field.

Findings

Demonstrates LLM agents' ability to source and integrate global event data.

Assesses agents' proficiency in code-based tool use for prediction.

Provides insights into short-term and long-term forecasting accuracy.

Abstract

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.