TradeVerse: A Longitudinal Benchmark of Political Negotiation in International Trade

A new benchmark, TradeVerse, has been developed to evaluate Large Language Models (LLMs) in the context of political negotiation and institutional texts. Unlike previous benchmarks that focused on isolated documents, TradeVerse addresses the longitudinal nature of real-world negotiations, where interactions evolve over time. It leverages World Trade Organization (WTO) specific trade concerns, reconstructing minutes from 1,170 meetings across 5 groups and 89 product groups to provide a dataset where turns are outcomes of prior interactions.

Why it matters

This development addresses a critical gap in the evaluation of AI systems for complex, real-world political and institutional interactions. Improving LLM performance in understanding and modeling longitudinal negotiations can enhance decision support, scenario planning, and analytical capabilities in international relations and policy-making.

Key insights

• Existing LLM benchmarks are limited in evaluating performance on complex, longitudinal political negotiations.
• Real-world negotiations involve multiple iterations where parties' arguments align or diverge, requiring tracking of preceding turns.
• TradeVerse is a new benchmark for LLMs that focuses on political negotiation within institutional contexts.
• The benchmark is built using data from the World Trade Organization (WTO)'s specific trade concerns.
• It reconstructs minutes from 1,170 meetings, covering 5 groups and 89 product groups, reflecting a multi-year negotiation process.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.06549