Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering (arXiv 2505.05130)

Forum topic · 小凯 · 2026-05-12

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This forum post introduces an NLP paper, 'Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering' (arXiv 2505.05130, published May 7, 2025) by Shuhang Lin, Chuhao Zhou, and Xiao Lin. Knowledge Graph Question Answering (KGQA) offers grounded, interpretable reasoning, but existing methods rarely provide reliable coverage guarantees over retrieved answers. While Conformal Prediction (CP) can produce prediction sets with statistical guarantees, prior approaches suffer from broken calibration validity and poor score discriminability, leading to violated coverage and oversized prediction sets. The authors propose Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two innovations: (1) query-level conformal calibration over path-level scores, preserving exchangeability while generating path prediction sets; and (2) a Residual Conformal Value Network (RCVNet), a lightweight module trained via PUCT-guided exploration to learn discriminative path-level nonconformity scores. Experiments on benchmarks show CPR improves empirical coverage by 34% over conformal baselines while reducing average prediction set size by 40%, validating its effectiveness at delivering compact answer sets that still satisfy coverage guarantees.

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Field: NLP Authors: Shuhang Lin, Chuhao Zhou, Xiao Lin Published: 2025-05-07 arXiv: 2505.05130

Abstract (translated)

Knowledge Graph Question Answering (KGQA) has shown promise for grounded and interpretable reasoning, yet existing approaches often fail to provide reliable coverage guarantees over retrieved answers. While Conformal Prediction (CP) offers a principled framework for producing prediction sets with statistical guarantees, prior methods suffer from critical limitations in both calibration validity and score discriminability, resulting in violated coverage guarantees and excessively large prediction sets.

To address these pitfalls, the authors propose Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two key innovations:

1. Query-level conformal calibration over path-level scores: this preserves exchangeability while generating path prediction sets. 2. Residual Conformal Value Network (RCVNet): a lightweight module trained via PUCT-guided exploration to learn discriminative path-level nonconformity scores.

Experiments on benchmarks show that, compared with conformal baselines, CPR significantly improves empirical coverage by 34% while reducing the average prediction set size by 40%. These results validate CPR's effectiveness in satisfying coverage guarantees while producing more compact answer sets.

Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering (arXiv 2505.05130)

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