The DOI matches its Crossref record; 246 of the 246 references it cites were found in a registry or an index.
The Revolution of Multimodal Large Language Models: A Survey
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Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara. Findings of the Association for Computational Linguistics: ACL 2024. 2024.
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“users should be aware that limitations such as introducing hallucination71 and omitting necessary signals73 are inherent to LLMs and may still impact classification performance, though MoMA is fine-tuned using groundtruth clinical labels.”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [73]
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Items 201–246 of 246
LLMGA: Multimodal Large Language Model based Generation Assistant
Xia, B., Wang, S., Tao, Y., et al. (2023) · European Conference on Computer Vision · doi:10.48550/arxiv.2311.16500 · Cited by 31 · cited 1×
“Similarly, in (Xia et al., 2023a) the LLM is trained to output detailed language-based generation prompts which are employed for generation or editing tasks.”
FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
Jing, L., Li, R., Chen, Y., & Du, X. (2023) · arXiv · doi:10.48550/arxiv.2311.01477 · Cited by 31
Tiny LVLM-eHub: Early Multimodal Experiments with BardPreprint
Shao, W., Hu, Y.-T., Gao, P., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2308.03729 · Cited by 30 · cited 1×
“…(i) the adaptation issue of switching from small images seen during training to larger ones at inference time and (ii) computational costs provided by the increased number of tokens in both the visual encoder and the LLM, given by the quadratic complexity of the attention-based architectures.”
UnIVAL: Unified Model for Image, Video, Audio and Language TasksPreprint
Shukor, M., Dancette, C., Ramé, A., & Cord, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.16184 · Cited by 29
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
Li, H., Li, S., Cai, D., et al. (2023) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2309.08637 · Cited by 29 · cited 1×
“However, recently there has been an interest in addressing more fine-grained visual tasks, such as visual grounding and image generation.”
BackgroundSong, Z., Wang, C., Sheng, J., Zhang, C., Tang, S., Fan, J., & Chen, T. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758252 · Cited by 28 · cited 1×
“Furthermore, effective strategies for visual instruction tuning are also designed in the video domain (Song et al., 2024), enabling more effective understanding of long video sequences.”
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality CollaborationPreprint
Ye, Q., Xu, H., Ye, J., Yan, M., Hu, A., Liu, H., Qian, Q., Zhang, J., Huang, F., & Zhou, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.04257 · Cited by 28
Fair Diffusion: Instructing Text-to-Image Generation Models on FairnessPreprint
Friedrich, F., Brack, M., Struppek, L., Hintersdorf, D., Schramowski, P., Luccioni, A. S., & Kristian Kersting, B. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.10893 · Cited by 28
See, Say, and Segment: Teaching LMMs to Overcome False Premises
Wu, T.-H., Biamby, G., Chan, D., Dunlap, L., Gupta, R., Wang, X., Gonzalez, J. E., & Darrell, T. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01278 · Cited by 26
Lenna: Language Enhanced Reasoning Detection Assistant
Wei, F., Zhang, X., Zhang, A., Zhang, B., & Chu, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888428 · Cited by 25 · cited 2×
“For output regions, LISA (Lai et al., 2023), GLaMM, GSVA (Xia et al., 2023b), NeXt-Chat (Zhang et al., 2023a), and LISA++ (Yang et al., 2023b) send the embedding corresponding to special tokens to the mask decoder of SAM, LLaVA-G to OpenSeeD (Zhang et al., 2023c), Lenna (Wei et al., 2023) to Grounding-DINO (Liu et al.…”
ChatterBox: Multi-round Multimodal Referring and GroundingPreprint
Tian, Y., Ma, T., Xie, L., Qiu, J., Tang, X., Zhang, Y., Jiao, J., Tian, Q., & Ye, Q. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.13307 · Cited by 23 · cited 2×
“ChatterBox (Tian et al., 2024b) combines features from the iTPN-B encoder (Tian et al., 2023) and the MLLM and provides them to the DINO detector (Zhang et al., 2022a).”
SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal InstructionsPreprint
Horawalavithana, S., Munikoti, S., Stewart, I., Kvinge, H., & Pazdernik, K. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.01139 · Cited by 23
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text
Zhu, W., Hessel, J., Awadalla, A., Gadre, S. Y., Dodge, J., Fang, A., Yu, Y., Schmidt, L., Wang, W. Y., & Choi, Y. (2023) · arXiv · doi:10.48550/arxiv.2304.06939 · Cited by 21 · cited 3×
“Among these datasets, the most used are WebLI (Chen et al., 2023j), composed of 10B images and image-text pairs, and MMC4 (Zhu et al., 2023d), an extension of the text-only C4 (Raffel et al., 2020) dataset composed of 365M documents and 156B tokens relatives to different concepts, and OBELICS…”
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language ModelPreprint
Xu, Z., Zhang, Y., Xie, E., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.01412 · Cited by 18
LLMBind: A Unified Modality-Task Integration FrameworkPreprint
Zhu, B., Ning, M., Jin, P., Lin, B., Huang, J., Song, Q., Zhang, J., Tang, Z., Pan, M., & Yuan, L. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.14891 · Cited by 18
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General HealthcarePreprint
Liu, J., Wang, Z., Ye, Q., Chong, D., Zhou, P., & Hua, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.17956 · Cited by 16
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIPreprint
Yue, X., Ni, Y., Zhang, K., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.16502 · Cited by 16
MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens
Zheng, K., He, X., & Wang, X. E. (2023) · arXiv · doi:10.48550/arxiv.2310.02239 · Cited by 15
InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding
Liu, H., You, Q., Han, X.-T., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2403.01487 · Cited by 15 · cited 1×
“InfiMM-HD (Liu et al., 2024a) proposes a dynamic resolution adaptation training stage to increase the image size up to 1,344 pixels.”
Position-Enhanced Visual Instruction Tuning for Multimodal Large Language ModelsPreprint
Chen, C., Qin, R., Luo, F., Mi, X., Li, P., Sun, M., & Liu, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.13437 · Cited by 13
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2310.02255 · Cited by 12 · cited 1×
“MathVista (Math V ) (Lu et al., 2023b) The evaluation score is computed by an LLM such as GPT-4, which is asked to compare a candidate answer with a reference one.”
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal ModelsPreprint
Li, Z., Yang, B., Liu, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.06607 · Cited by 10
Feng, H., Liu, Q., Liu, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.11810 · Cited by 7
Contextual Object Detection with Multimodal Large Language ModelsPreprint
Zang, Y., Li, W., Han, J. S., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.18279 · Cited by 6
VILA: On Pre-training for Visual Language ModelsPreprint
Ji, L., Yin, H., Ping, W., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.07533 · Cited by 6
FoodLMM: A Versatile Food Assistant using Large Multi-modal ModelPreprint
Yin, Y., Qi, H., Zhu, B., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.14991 · Cited by 6
Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMsPreprint
Xuan, S., Guo, Q., Yang, M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.00582 · Cited by 4
LLaFS: When Large Language Models Meet Few-Shot SegmentationPreprint
Zhu, L., Chen, T., Ji, D., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.16926 · Cited by 4
Honeybee: Locality-enhanced Projector for Multimodal LLMPreprint
Cha, J., Kang, W., Mun, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.06742 · Cited by 3
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution ImagesPreprint
Xu, R., Yao, Y., Guo, Z., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.11703 · Cited by 3
BT-Adapter: Video Conversation is Feasible Without Video Instruction TuningPreprint
Liu, R., Li, C., Ge, Y., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.15785 · Cited by 2
GSVA: Generalized Segmentation via Multimodal Large Language ModelsPreprint
Xia, Z., Han, D., Han, Y., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.10103 · Cited by 2
LION : Empowering Multimodal Large Language Model with Dual-Level Visual KnowledgePreprint
Chen, G., Shen, L., Shao, R., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.11860 · Cited by 1
Chen, Y., Sikka, K., Cogswell, M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.10081 · Cited by 1
mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language ModelPreprint
Hu, A., Shi, Y., Xu, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.18248 · Cited by 1
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and PlanningPreprint
Chen, S., Chen, X., Zhang, C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.18651 · Cited by 1
Dolphins: Multimodal Language Model for DrivingPreprint
Ma, Y., Cao, Y., Sun, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.00438 · Cited by 1
Lenna: Language Enhanced Reasoning Detection AssistantPreprint
Wei, F., Zhang, X., Zhang, A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.02433 · Cited by 1
See, Say, and Segment: Teaching LMMs to Overcome False PremisesPreprint
Wu, T.-H., Biamby, G., Chan, D. M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.08366 · Cited by 1
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
Feng, J., Sun, Q., Xu, C., Zhao, P., Yang, Y., Tao, C., Zhao, D., & Lin, Q. (2022) · arXiv · doi:10.48550/arxiv.2211.05719 · cited 4×
“Given the amount of training data currently available, approaches like SPHINX-X (Gao et al., 2024) opt to perform a single all-in-one training stage in which to update all model components, possibly also using text-only data to preserve the conversational capabilities of the LLM.”
BackgroundMethodInfluentialSmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language ModelsPreprint
Huang, Y., Xie, L., Wang, X., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.06739
Pixel Aligned Language ModelsPreprint
Xu, J., Zhou, X., Shen, Y., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.09237
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774 · cited 13×
“3B ♢ CLIP ViT-L Q-Former ✻ ✗ Visual Dialogue, VQA, Captioning LLaMA-Adapter V2 (Gao et al., 2023) LLaMA-7B ▲ CLIP ViT-L Linear ✗ VQA, Captioning OpenFlamingo (Awadalla et al., 2023) MPT-7B ⋆ CLIP ViT-L XAttn LLM ✗ VQA, Captioning Flamingo (Alayrac et al., 2022) Chinchilla-70B ⋆ NFNet-F6 XAttn LLM ✗ Visual Dialogue, VQ…”
InfluentialOPT: Open Pre-trained Transformer Language Models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068 · cited 2×
“Al-ternatives are OPT (Zhang et al., 2022b), Mag-neto (Wang et al., 2023b), MPT (MosaicML, 2023), and the instruction-tuned (Chung et al., 2022) or multilingual (Xue et al., 2020) flavors of T5 (Raffel et al., 2020), an encoder-decoder language model pre-trained for multiple tasks. et al., 2024; Zhao et al., 2024).”
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.13394 · cited 12×
“TextVQA (VQA T ) (Singh et al., 2019) is a dataset based on pictures from Open Images (Kuznetsova et al., 2020) Comprehensively describing the visual input is another important skill desired in MLLMs.”
InfluentialEvaluating Object Hallucination in Large Vision-Language Models
Li, Y., Du, Y., Zhou, K., Wang, J., Zhao, W. X., & Wen, J.-R. (2023) · arXiv · doi:10.48550/arxiv.2305.10355
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“Employing Multi-modal Large Language Models 163 , next-generation smart glasses not only enhance the quality of real-time decision-making 164 but also deliver personalized suggestions 165 derived from aggregated life and dietary data.”
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Self-citationSafe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Poppi, S., Poppi, T., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_20 · Cited by 35
Self-citation
Metadata
- Authors
- Caffagni, D., Cocchi, F., Barsellotti, L., Moratelli, N., Sarto, S., Baraldi, L., Baraldi, L., Cornia, M., & Cucchiara, R.
- Affiliations
- University of Modena and Reggio Emilia (IT)
- University of Pisa (IT)
- ORCID
- Venue
- Findings of the Association for Computational Linguistics ACL 2024
- Published
- 2024
- Pages
- 13590-13618
- OpenAlex
- W4402670262
- Cited by
- 98
- References
- 171
- Citations by year
- 2026: 44 · 2025: 46 · 2024: 8
- FWCI
- 20.26
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- Funders
- European Commission (PNRR-M4C2, PE00000013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- multimodal large language models, multimodal language models, vision-language models, large language models, multimodal representation learning
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