Trification: A Comprehensive Tree-based Strategy Planner and Structural Verification for Fact-Checking

ACL ARR 2026 January Submission1447 Authors

Keywords: fact checking

Abstract:

Technological advancements allow information to be shared with a single click, which has enabled the rapid spread of false information.
This makes automated fact-checking systems necessary to ensure the safety and integrity of our online media ecosystem.
Previous methods have demonstrated the effectiveness of decomposing the claim into simpler sub-tasks and utilizing LLM-based multi-agent system to execute them.
However, those models face two limitations: they often fail to verify every component of a claim and lack a structured framework to logically connect the results of sub-tasks for a final prediction.
In this work, we propose a novel automated fact-checking framework called \texttt{Trification}.
Our framework begins by generating a comprehensive set of verification actions to ensure complete coverage of the claim.
It then structures these actions into a dependency graph to model the logical relationships between actions.
Furthermore, the graph can be dynamically modified, allowing the system to adapt its verification strategy.
Experimental results on two challenging benchmarks demonstrate that our framework significantly enhances fact-checking accuracy, thereby advancing current state-of-the-art in automated fact-checking systems.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: fact checking

Languages Studied: English

Submission Number: 1447