Papers with FactCorrector approach

1 papers
FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models (2026.acl-long)

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Challenge: Large language models (LLMs) often produce factually incorrect responses.
Approach: They propose a new method that adapts across domains without retraining and leverages structured feedback to generate a correction.
Outcome: The proposed method outperforms baseline methods on a VELI5 dataset and several popular long-form factuality datasets.

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