arXiv:2608. 05228v1 Announce Type: new Abstract: The "decompose-then-verify" paradigm for LLM factuality evaluation faces a fundamental trade-off: atomic facts, i.
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ElementCheck is a new framework for evaluating the factuality of long-form text that addresses limitations of the traditional decompose‑retrieve‑verify pipeline. Rather than breaking sentences into atomic sub‑claims, it extracts entity pairs linked by verifiable connections to form an element graph, using the graph’s topology to gauge sentence complexity. This allows simple sentences to be verified directly while complex ones undergo targeted element‑level refinement, and the authors introduce the FastFact‑Sent benchmark to support fine‑grained evaluation, demonstrating consistent improvements across multiple backbone models.
Systematically evaluating the factuality of large language models with the FACTS Benchmark Suite.
AEScorer is an agentic evidence‑grounded framework designed for graded factuality verification, addressing the limitation of binary judgments in current methods. It operates in two stages: first, it gathers and refines external evidence through agentic search; second, it predicts a scalar factuality score to capture nuanced differences in correctness. The authors also introduce GradedVeriBench, a benchmark covering general and multi‑hop question answering, and demonstrate that AEScorer outperforms existing approaches on this new benchmark.
The paper introduces Fact-Ablated Evaluation (FAE), a framework that iteratively removes cited evidence to test whether large language models (LLMs) adjust their fact‑checking predictions accordingly. Experiments reveal that many off‑the‑shelf LLMs rely more on internal knowledge than on the provided evidence. To address this, the authors propose REAL, a training method that uses counterfactual evidence supervision to encourage LLMs to base veracity judgments on evidence, achieving better evidence‑dependent performance across four datasets.
arXiv:2609.00241v1 Announce Type: new Abstract: Long documents often distribute important information across extensive narrative passages and multiple tables, making faithful summarization particular...
arXiv:2606. 19819v1 Announce Type: cross Abstract: Decomposing compound sentences into atomic, verifiable claims is a prerequisite for reliable automated fact-checking.
