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arXiv Machine Learning By Hanghui Guo, Shimin Di, Pasquale De Meo, Zhangze Chen, Jia Zhu

MuPlon: Multi-Path Causal Optimization for Claim Verification through Controlling Confounding

Read the original on arXiv Machine Learning →

MuPlon is a new framework for claim verification that models claims and evidence as a fully connected Claim‑Evidence Graph. It tackles two confounding problems—data noise and data biases—by applying a dual causal intervention strategy: a back‑door path that adjusts node weights to reduce noise and strengthen relevant connections, and a front‑door path that extracts key subgraphs, builds reasoning paths, and uses counterfactual reasoning to remove biases. Experiments show that MuPlon surpasses existing methods and achieves state‑of‑the‑art performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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