Summary of Calibrated Self-rewarding Vision Language Models, by Yiyang Zhou et al.
Calibrated Self-Rewarding Vision Language Models
by Yiyang Zhou, Zhiyuan Fan, Dongjie Cheng, Sihan Yang, Zhaorun Chen, Chenhang Cui, Xiyao Wang, Yun Li, Linjun Zhang, Huaxiu Yao
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach called Calibrated Self-Rewarding (CSR) to address the hallucination phenomenon in Large Vision-Language Models (LVLMs). LVLMs integrate pre-trained language models and vision models through instruction tuning, but often prioritize textual information over visual input. Existing methods use additional models or human annotations to curate preference data for fine-tuning, but these may not effectively reflect the target LVLM’s preferences. CSR enables the model to self-improve by iteratively generating candidate responses, evaluating rewards, and curating preference data for fine-tuning. The approach employs a step-wise strategy and incorporates visual constraints into the self-rewarding process to emphasize visual input. Experimental results demonstrate that CSR enhances performance and reduces hallucinations across ten benchmarks and tasks, achieving significant improvements over existing methods by 7.62%. Theoretical analysis verifies the effectiveness of introducing visual constraints into the self-rewarding paradigm. CSR also shows compatibility with different vision-language models and incremental improvement through fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that computer programs can understand both pictures and words better. Right now, these programs often get confused between what’s in a picture and what someone says about it. The authors of this paper came up with an idea called Calibrated Self-Rewarding to help the program learn from its mistakes and become more accurate. They tested their idea on lots of different tasks and found that it worked really well, reducing errors by 7.62%. This is important because it could be used in all sorts of applications, like helping people with disabilities or creating better image recognition systems. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Instruction tuning
