Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization

TL;DR

This paper introduces a Structural Causal Model-based framework with Group Relative Policy Optimization to improve multi-hop fact verification accuracy and interpretability in large language models.

Contribution

It proposes a novel SCM grounding and a GRPO-based reinforcement learning method to balance reasoning complexity and accuracy in fact verification.

Findings

Identifies an inverted U-shaped relationship between reasoning chain length and accuracy.

Demonstrates significant performance improvements over state-of-the-art baselines on HoVer and EX-FEVER datasets.

Abstract

Multi-Hop Fact Verification (MHFV) necessitates complex reasoning across disparate evidence, posing significant challenges for Large Language Models (LLMs) which often suffer from hallucinations and fractured logical chains. Existing methods, while improving transparency via Chain-of-Thought (CoT), lack explicit modeling of the causal dependencies between evidence and claims. In this work, we introduce a novel framework that grounds reasoning in a Structural Causal Model (SCM), treating verification as a constructive causal inference process. We empirically identify an "inverted U-shaped" correlation between reasoning chain length and accuracy, revealing that excessive structural complexity degrades performance. To address this, we propose a Rule-based Reinforcement Learning strategy using Group Relative Policy Optimization (GRPO). This approach dynamically optimizes the trade-off between structural depth and conciseness. Extensive experiments on HoVer and EX-FEVER demonstrate that our SCM-GRPO framework significantly outperforms state-of-the-art baselines, offering a reliable and interpretable solution for complex fact verification.