  Generative AI models exhibit remarkable potential; however, hallucinations
across various tasks present a significant challenge, particularly for longer
inputs that current approaches struggle to address effectively. We introduce
SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a
task-agnostic model for detecting factual inconsistencies using a novel
chunking strategy. Specifically, SCALE is a Natural Language Inference (NLI)
based model that uses large text chunks to condition over long texts. This
approach achieves state-of-the-art performance in factual inconsistency
detection for diverse tasks and long inputs. Additionally, we leverage the
chunking mechanism and employ a novel algorithm to explain SCALE's decisions
through relevant source sentence retrieval. Our evaluations reveal that SCALE
outperforms existing methods on both standard benchmarks and a new long-form
dialogue dataset ScreenEval we constructed. Moreover, SCALE surpasses
competitive systems in efficiency and model explanation evaluations. We have
released our code and data publicly to GitHub.
