Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models
Summary: arXiv:2604.10985v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new and more capable LLMs emerge with improved reasoning, instruction-following, and generalization, there is a pressing need to efficiently update existing VLMs to incorporate these advancements. However, the integration of new LLMs into VLMs, particularly how the evolving LLMs contribute to multimodal reasoning, alignment, and task-specific performance remains underexplored. Addressing this gap is important for VLM development, given the rapid evolution of pretrained LLM backbones.
This study presents a controlled and systematic investigation of how changes in the pretrained LLM backbone affect downstream VLM task performance. By having the vision encoder, training data, and post-training algorithm remain the same across LLAMA-1, LLAMA-2, and LLAMA-3 based VLMs, we find that newer LLM backbones do not always lead to better VLMs, but the performance depends on the downstream VLM task.
Key Findings
- In visual question and answering tasks, newer LLM backbones tend to solve different questions rather than just more questions.
- The analysis indicates that performance differences are driven by how models process information.
- Better calibrated confidence and more stable internal representations are observed with newer LLMs.
- Some VLM capabilities are exclusive to the newest LLM generation.
- Tasks that primarily rely on visual understanding show minimal benefit from newer LLM backbones.
Implications for Future Research
The findings emphasize the necessity for a deeper understanding of how pretrained LLMs influence the performance of VLMs across various tasks. This research is crucial as the field continues to evolve and adapt to new technologies. Researchers and developers need to consider the specific requirements of VLM applications to determine whether integrating a newer LLM backbone will yield significant advantages.
Future studies should focus on:
- Exploring the mechanisms behind the performance variations across different LLM backbones.
- Investigating the specific characteristics of tasks that benefit from newer LLMs.
- Establishing benchmarks for evaluating VLM performance across various LLM backbones.
Conclusion
As the landscape of pretrained LLMs continues to evolve, understanding their impact on downstream VLM tasks is essential for maximizing the potential of these technologies. The insights gained from this research will aid in crafting more effective and efficient VLMs, contributing to advancements in AI applications that utilize vision and language integration.