Advancing Visual Quality Comparison with Co-Instruct Dataset

Główne pojęcia

The author introduces the Co-Instruct dataset to enhance visual quality comparison by providing open-ended settings and detailed reasoning, surpassing existing benchmarks and outperforming state-of-the-art models.

Streszczenie

The content discusses the development of the Co-Instruct dataset for open-ended visual quality comparison. It highlights the methodology, data construction, model adaptations, evaluation results against baseline models, and performance on various benchmarks. The Co-Instruct dataset significantly improves comparative capabilities and outperforms existing LMMs and even proprietary models in visual quality assessment.

Statystyki

Comparative settings standardize evaluation criteria.
Co-Instruct achieves 30% higher accuracy than other LMMs.
MICBench contains 2,000 MCQs for multi-image comparison.
GPT-4V responses used as pseudo labels in Teach2Compare.
Co-Instruct shows improvements over baseline models in various benchmarks.

Cytaty

"The first image has better quality than the second image."
"The proposed model is only inferior on the MM21 dataset."
"Co-Instruct outperforms all existing models in 2AFC-LMM."

Kluczowe wnioski z

Towards Open-ended Visual Quality Comparison

by Haoning Wu,H... o arxiv.org 03-05-2024

https://arxiv.org/pdf/2402.16641.pdf

Głębsze pytania

How can collaborative teaching strategies improve model performance?

Collaborative teaching strategies can enhance model performance by leveraging the strengths of different sources of information. In the context of the Co-Instruct model, combining data from Merge2Compare and Teach2Compare allows for a more comprehensive training dataset. Merge2Compare provides accurate comparisons with high precision, while Teach2Compare offers diverse scenarios and content-rich information. By integrating these subsets through collaborative instruction tuning, the Co-Instruct model benefits from a wider range of quality comparison examples, leading to improved learning outcomes.

What are the implications of surpassing human capability in visual quality comparisons?

Surpassing human capability in visual quality comparisons has significant implications for automated systems and artificial intelligence applications. It indicates that models like Co-Instruct have advanced to a level where they can outperform humans in specific tasks related to visual quality assessment. This achievement suggests that AI systems can provide more consistent and reliable evaluations than humans, reducing subjectivity and potential biases in assessments. Additionally, it opens up possibilities for using AI-driven solutions in real-world scenarios where precise and objective quality judgments are crucial.

How might biases affect the evaluation of long text outputs in LMMs?

Biases can impact the evaluation of long text outputs in Language Model Models (LMMs) by influencing how these outputs are perceived or interpreted. Biases may stem from various sources such as training data imbalances, preconceived notions embedded within models, or inherent limitations within algorithms themselves. In the case of evaluating long text outputs, biases could manifest as preferences towards longer responses being perceived as more informative or accurate simply due to their length rather than actual content quality. This bias could potentially skew evaluation metrics towards favoring verbosity over substance if not carefully accounted for during assessment processes.