Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges
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A fine-tuned large language-vision model achieves 98% accuracy on visual question answering for military vehicle identification in SAR imagery from an extended MSTAR benchmark.
Parallel chunk processing with evidence-anchored consolidation reduces omission errors by 84%, boosts traceability by 130%, and cuts unsupported claims by 91% in LLM long-document analysis.
citing papers explorer
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Effects of Generative AI Errors on User Reliance Across Task Difficulty
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
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Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition
A fine-tuned large language-vision model achieves 98% accuracy on visual question answering for military vehicle identification in SAR imagery from an extended MSTAR benchmark.
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Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
Parallel chunk processing with evidence-anchored consolidation reduces omission errors by 84%, boosts traceability by 130%, and cuts unsupported claims by 91% in LLM long-document analysis.