Papers with multimodal instruction tuning
Multimodal Instruction Tuning with Conditional Mixture of LoRA (2024.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in diverse tasks across different domains. |
| Approach: | They propose a method that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA. |
| Outcome: | Experimental results show that MixLoRA outperforms LoRA with the same or higher ranks . MLLMs have demonstrated remarkable proficiency in diverse tasks across domains . |
An Examination of the Compositionality of Large Generative Vision-Language Models (2024.naacl-long)
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| Challenge: | Recent studies have focused on the compositionality of vision-language models (VLMs) however, the performance of GVLMs in multimodal compositional reasoning remains under-explored. |
| Approach: | They propose a syntactical bias score to quantify GVLMs' syntaktical bias . they propose 'SADE' task to assess GVLs's robustness against inclination toward syntical correctness. |
| Outcome: | The proposed benchmarks are based on evaluation metrics and current benchmarks. |
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning (2023.acl-long)
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| Challenge: | Experimental results show zero-shot performance on unseen multimodal tasks . instruction tuning has yet to be explored for vision and multimodal task. |
| Approach: | They propose a multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. |
| Outcome: | The proposed model performs well on unseen multimodal tasks and is highly scalable. |
FTibSuite: A Comprehensive Resource Suite for Tibetan Vision–Language Modeling (2026.findings-acl)
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| Challenge: | FTibSuite provides an end-to-end training-and-evaluation workflow for vision–language models . Tibetan is underserved due to the lack of infrastructure for reproducible training and evaluation. |
| Approach: | They propose a resource-centric workflow for Tibetan VLMs that provides an end-to-end training-and-evaluation workflow and human-verified multimodal annotations. |
| Outcome: | FTibSuite provides an end-to-end training-and-evaluation workflow and human-verified multimodal annotations. |