Causal Models and Reinforcement Learning Enhance LLM Multi-Hop Fact Verification

Deep Intelligence Analysis

Visual Intelligence

flowchart LR
A[Multi-Hop Fact Verification] --> B{LLM Hallucinations}
B --> C[Fractured Logic]
C --> D[Structural Causal Model]
D --> E[Causal Inference Process]
E --> F[Group Relative Policy Optimization]
F --> G[Optimized Reasoning Chain]
G --> H[Reliable Fact Verification]

Auto-generated diagram · AI-interpreted flow

Impact Assessment

Large Language Models frequently struggle with multi-hop fact verification, often generating hallucinations or fragmented logical chains. This new framework, by explicitly modeling causal dependencies and optimizing reasoning chain length, offers a robust and interpretable solution. This is critical for improving the reliability of LLMs in high-stakes applications where factual accuracy and transparent reasoning are paramount.

Key Details

• Multi-Hop Fact Verification (MHFV) challenges LLMs with hallucinations and fractured logic.
• The new framework grounds reasoning in a Structural Causal Model (SCM).
• Verification is treated as a constructive causal inference process.
• An 'inverted U-shaped' correlation between reasoning chain length and accuracy was identified.
• Group Relative Policy Optimization (GRPO) is proposed for dynamic optimization.
• SCM-GRPO significantly outperforms state-of-the-art baselines on HoVer and EX-FEVER datasets.