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MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning., , , , , , , and . CoRR, (2023)Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey., , , , , , , , , and 3 other author(s). CoRR, (2024)HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models., , , , , , and . CoRR, (2023)MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning., , , , , , , and . NAACL-HLT, page 1287-1310. Association for Computational Linguistics, (2024)Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning., , , , , and . ICLR, OpenReview.net, (2024)Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences., , , , , , , , , and 3 other author(s). ACL (1), page 416-442. Association for Computational Linguistics, (2024)COVID-VTS: Fact Extraction and Verification on Short Video Platforms., , and . EACL, page 178-188. Association for Computational Linguistics, (2023)Visual News: Benchmark and Challenges in News Image Captioning., , , and . EMNLP (1), page 6761-6771. Association for Computational Linguistics, (2021)Mosaic IT: Enhancing Instruction Tuning with Data Mosaics., , , , , , , and . CoRR, (2024)