Bridging the Logic Gap in Fact-Checking through Content–Logic Coupled Evaluation Paradigm

ACL ARR 2026 January Submission9401 Authors

Keywords: Fact-checking, Long-form Fact-checking Dataset, Content-Logic Coupled Factuality Evaluation

Abstract:

Driven by the rise of social media and generative artificial intelligence, the internet has been flooded with unverified information, making reliable fact-checking increasingly critical.
Most existing fact-checking research adheres to the decompose-then-verify paradigm, emphasizing verification of individual facts while overlooking the validity of logical dependencies among them. As a result, text containing logical errors may still be misjudged as factual.
Moreover, existing datasets and metrics focus on fact completeness and coverage, failing to capture the logical dimension.
To help bridge this gap, we propose a content–logic coupled factuality evaluation paradigm, which conceptualizes factuality along two complementary dimensions: content factuality and logic factuality.
Under this paradigm, we introduce a holistic solution consisting of \textsc{LoReFact}, the first long-form fact-checking dataset that systematically incorporates the logical dimension; LoRe-Factcheck, a simple yet effective framework for joint content–logic evaluation; and a logic-aware metric named LoReFactScore for exposing and penalizing logical fallacies.
Experiments demonstrate the importance of logical factuality and the effectiveness of our proposed paradigm for fact-checking.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: evaluation methodologies,NLP datasets,evaluation,metrics

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 9401