Inconsistency Detection

Inconsistency detection focuses on identifying discrepancies between different data sources or model outputs, aiming to improve the reliability and trustworthiness of information systems. Current research emphasizes the application of large language models (LLMs) and transformer-based architectures, along with techniques like natural language inference (NLI) and semantic role labeling, to detect inconsistencies across diverse modalities, including text, images, and audio. This field is crucial for enhancing the robustness of AI systems, particularly in applications like automatic summarization, object detection, and deepfake detection, where factual accuracy and reliability are paramount. Improved inconsistency detection methods will lead to more trustworthy AI systems and mitigate the spread of misinformation.