Establishing LMM benchmarks to evaluate multimodal data processing for optical character recognition (OCR), object hallucination, and adversarial robustness to create higher performing models is a priority . For instance, the 2023 MMHAL-BENCH saw six multi-modal models having the majority of the scores scoring less than 3.0/4.0 over 9 metrics . In addition, benchmarks such as the 2024 OCRBench assesses strengths and weaknesses of these models in a variety of data formats and mathematical expressions . Benchmarks continuing throughout the rest of 2024 aim to further reduce LMM hallucinations that are classified as either a) Input-conflicting (answer deviates from input) b) Context-conflicting (output contradicts LLM-generation), or c) Fact-conflicting (content misaligns with established knowledge) .