Blinded by Context: Unveiling the Halo Effect of MLLM in AI Hiring (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) and Multimodal Large Language Modells (MLLMs) are increasingly being deployed across a range of domains, including finance, law, peer review, and recruitment. |
| Approach: | They investigated how image-based evaluations are influenced by non-job-related information, including extracurricular activities and social media images. |
| Outcome: | The proposed models exhibit significant halo effects in image-based evaluations while text-based assessments showed more resistance to bias. |
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| Challenge: | Recent advances in Large Language Models have facilitated the development of Multimodal LLMs. |
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How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning? (2024.emnlp-main)
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| Challenge: | MLLMs have significant capabilities for multimodal in-context learning, but their effectiveness hinges on the appropriate selection of in-constext examples. |
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Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios (2025.emnlp-main)
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Yunkai Dang, Mengxi Gao, Yibo Yan, Xin Zou, Yanggan Gu, Jungang Li, Jingyu Wang, Peijie Jiang, Aiwei Liu, Jia Liu, Xuming Hu
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Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Multimodal large language models combine visual and textual data for tasks like image captioning and visual question answering. |
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MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria (2025.naacl-long)
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Wentao Ge, Shunian Chen, Hardy Chen, Nuo Chen, Junying Chen, Zhihong Chen, Wenya Xie, Shuo Yan, ChenghaoZhu ChenghaoZhu, Ziyue Lin, Dingjie Song, Xidong Wang, Anningzhe Gao, Zhang Zhiyi, Jianquan Li, Xiang Wan, Benyou Wang
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From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024.lrec-tutorials)
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| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
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| Challenge: | Multimodal large language models exhibit a pronounced form of visual sycophantic behavior when they process image inputs. |
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CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships? (2025.acl-long)
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| Challenge: | Existing benchmarks focus on assessing factual and logical correctness in downstream tasks with limited emphasis on evaluating MLLMs’ ability to interpret pragmatic cues and intermodal relationships. |
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Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations (2026.findings-eacl)
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| Challenge: | Recent advances extend language understanding beyond text to speech, enabling unified reasoning across modalities. |
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CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)
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Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu
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