Investigating the Scaling Effect of Instruction Templates for Training Multimodal Language Model

Abstract: Current multimodal LLM (MLM) training approaches overlook the influence of instruction templates. Previous research deals with this problem by leveraging hand-crafted or model-generated templates, failing to investigate the scaling effect of instruction templates on MLM training. In this work, we propose a programmatic instruction template generator capable of producing over 15K unique instruction templates by filling randomly sampled positional synonyms into weighted sampled meta templates, enabling us to comprehensively explore MLM's performance across various template scales in the training process. Our investigation into scaling instruction templates for MLM training demonstrates that MLM capabilities do not consistently improve with increasing template scale. Instead, optimal performance is achieved at a medium template scale. Models trained with data augmented at the optimal template scale achieve performance gains of up to 10% over those trained on the original data and achieve the best overall performance compared with the similar-scale MLMs tuned on at most 75 times the scale of our augmented dataset. The code will be publicly available at https://github.com/shijian2001/TemplateScaling.

• The paper introduces a programmatic instruction template generator and reveals that MLMs exhibit significant sensitivity to template variations, impacting evaluation results.
• Empirical evaluation across various models and datasets shows performance disparities up to 29% due to different instruction formats, indicating a crucial need for diverse evaluation templates.
• Diversifying instruction templates during training can significantly improve MLM performance and robustness without increasing dataset size, outperforming models trained on much larger datasets.

Template Matters in Multimodal LLMs: Evaluation and Training

The paper "Template Matters: Understanding the Role of Instruction Templates in Multimodal LLM Evaluation and Training" addresses a critical issue in MLMs— the significant impact that instruction templates have on model performance during evaluation and training. The authors have systematically approached this often-overlooked aspect by introducing a programmatic instruction template generator, providing robust insights into MLM sensitivity to template variations.

Key Contributions

The authors present a novel approach, employing a programmatic generator capable of creating over 39 billion unique instruction template combinations. This tool allows for a detailed examination of model performance across an extensive array of template formats. Their empirical findings demonstrate that MLMs exhibit significant sensitivity to template variations, with performance disparities of up to 29% being observed across different instruction formats.

Evaluation and Insights

The study evaluates eight prevalent MLMs using five benchmark datasets, revealing substantial performance inconsistencies when models are subjected to different templates. For example, the InternVL-Chat-1.5-24B model demonstrates notable sensitivity, with a 29% performance gap across various templates on the MMBench dataset. These results highlight the crucial need for evaluations involving a diverse range of instruction templates to secure more reliable model assessments.

Instruction Template Sensitivity

The paper uncovers that MLMs' sensitivity to instruction format persists notwithstanding model scale or conventional vision instruction tuning. This suggests an enduring vulnerability to template design, prompting reevaluation of existing vision instruction methods. Additionally, the authors find that commonly used simple templates often underestimate the performance fluctuation inherent in these models.

Enhancing MLM Performance

Addressing the illustrated deficiencies, the paper proposes an effective method to improve vision instruction tuning by diversifying instruction data through the augmentation of instruction templates, without enlarging the training dataset. When applied to LLaVA-1.5 models, this method achieves superior performance, successfully outperforming models trained on data up to 75 times larger in scale. This demonstrates the potential of intelligent data utilization in optimizing MLM capabilities.

Implications and Future Directions

These findings prompt substantial implications for both the practical deployment and theoretical understanding of MLMs. Practically, enhancing robustness through diverse template exposure is indicated as imperative to improve real-world applications. Theoretically, this study underscores a shift toward template diversification as a fundamental consideration during model evaluation and tuning, which could redefine best practices and standard benchmarks within the field.

For future work, expanding to budget-constrained optimization of instruction templates tailored to specific models and tasks could yield further insights. Enhanced generalization of template-augmented training to overcome balance issues across datasets is another promising direction.

Conclusion

Overall, this paper makes significant strides in addressing the "elephant-in-the-room" issue of instruction templates within MLM evaluation and training. By focusing on the impact and optimization of these templates, it sets a foundation for developing more robust and reliable models, opening pathways for subsequent exploration of adaptive, model-specific template strategies. Such research continues to be foundational in advancing multimodal machine learning and its diverse applications.