This research paper introduces GLOV, a novel method employing LLMs to optimize prompts for VLMs, thereby enhancing their performance on downstream vision tasks.
Research Objective: The study aims to improve VLM performance on tasks like image classification by using LLMs to discover optimal prompts, moving away from traditional gradient-based optimization.
Methodology: GLOV utilizes a meta-prompt containing system instructions, task descriptions, and ranked in-context examples (previously generated prompts with their accuracies). This meta-prompt guides the LLM to generate new prompts iteratively. The effectiveness of each generated prompt is evaluated using a fitness function based on classification accuracy on a held-out training set. Furthermore, GLOV incorporates a novel guidance mechanism that steers the LLM's generation process by adding a hidden state offset vector, derived from the difference between positive and negative prompt embeddings, to the LLM's activation space. This guides the LLM towards generating prompts preferred by the downstream VLM.
Key Findings: GLOV demonstrates significant performance improvements across 16 diverse datasets using both dual-encoder (e.g., CLIP) and encoder-decoder (e.g., LLaVa) VLM architectures. For dual-encoder models, GLOV achieves accuracy improvements of up to 15.0% (3.81% on average), while for encoder-decoder models, the improvements reach up to 57.5% (21.6% on average). The study also highlights the importance of the guidance mechanism in achieving these improvements.
Main Conclusions: The research concludes that LLMs can effectively function as implicit optimizers for VLMs, discovering highly performant prompts without the need for gradient-based learning. The proposed GLOV method, particularly with its guidance mechanism, offers a promising avenue for enhancing VLM performance on various vision tasks.
Significance: This research significantly contributes to the field of vision-language modeling by presenting a novel and effective method for prompt optimization. It opens up new possibilities for improving VLM performance and broadening their application in real-world scenarios.
Limitations and Future Research: The study primarily focuses on image classification tasks. Future research could explore GLOV's applicability to other vision-language tasks like visual question answering and image captioning. Additionally, investigating the impact of different LLM architectures and guidance mechanisms on GLOV's performance could be beneficial.
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by M. Jehanzeb ... at arxiv.org 10-10-2024
https://arxiv.org/pdf/2410.06154.pdfDeeper Inquiries