Papers with plug-and-play technique
UniRAG: Universal Retrieval Augmentation for Large Vision Language Models (2025.findings-naacl)
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| Challenge: | Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding and MM generation. |
| Approach: | They propose a plug-and-play technique that adds relevant retrieved information to prompts as few-shot examples during inference. |
| Outcome: | The proposed method significantly improves the output quality of large vision language models when input prompts are augmented with relevant information retrieved by Vision-Language retrievers like UniRAG. |
Harnessing Consistency for Robust Test-Time LLM Ensemble (2026.findings-eacl)
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Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu, Xuying Ning, Tianxin Wei, Yuchen Yan, Jingrui He, Hanghang Tong
| Challenge: | Existing efforts to improve LLM ensemble quality have focused on model consistency, but failures are often due to heterogeneous tokenization schemes and varying model expertise. |
| Approach: | They propose a plug-and-play technique that harnesses model consistency for robust LLM ensemble. |
| Outcome: | The proposed technique improves ensemble performance and robustness against erroneous signals. |
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs? (2024.naacl-long)
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Yan Zeng, Hanbo Zhang, Jiani Zheng, Jiangnan Xia, Guoqiang Wei, Yang Wei, Yuchen Zhang, Tao Kong, Ruihua Song
| Challenge: | Recent advances in GPT-4V have demonstrated remarkable multi-modal capabilities in processing image inputs and following open-ended instructions. |
| Approach: | They propose a plug-and-play technique to enhance multi-modal LLMs . they propose 'lynx' to train multi-modal LLM models . |
| Outcome: | The proposed training strategy improves understanding accuracy and instruction-following proficiency of multi-modal models. |