GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models (2410.06154v5)

Abstract: In this work, we propose GLOV, which enables LLMs to act as implicit optimizers for Vision-LLMs (VLMs) to enhance downstream vision tasks. GLOV prompts an LLM with the downstream task description, querying it for suitable VLM prompts (e.g., for zero-shot classification with CLIP). These prompts are ranked according to their fitness for the downstream vision task. In each respective optimization step, the ranked prompts are fed as in-context examples (with their accuracies) to equip the LLM with the knowledge of the type of prompts preferred by the downstream VLM. Furthermore, we explicitly guide the LLM's generation at each optimization step by adding an offset vector -- calculated from the embedding differences between previous positive and negative solutions -- to the intermediate layer of the network for the next generation. This offset vector biases the LLM generation toward the type of language the downstream VLM prefers, resulting in enhanced performance on the downstream vision tasks. We comprehensively evaluate our GLOV on two tasks: object recognition and the critical task of enhancing VLM safety. Our GLOV shows performance improvement by up to 15.0% and 57.5% for dual-encoder (e.g., CLIP) and encoder-decoder (e.g., LlaVA) models for object recognition and reduces the attack success rate (ASR) on state-of-the-art VLMs by up to $60.7\%$.

• The paper introduces GLOV, a framework that uses LLMs as implicit optimizers to enhance VLM performance without any parameter updates.
• It utilizes meta-prompting to generate and refine prompts for models like CLIP and LLaVa across zero-shot and few-shot scenarios.
• Empirical evaluations demonstrate up to 15% improvement for dual-encoder models and 57.5% for encoder-decoder models, highlighting its efficiency.

Overview of Guided LLMs as Implicit Optimizers for Vision LLMs

The paper "GLOV: Guided LLMs as Implicit Optimizers for Vision LLMs" presents an innovative framework, named GLOV, that leverages LLMs for optimizing Vision-LLMs (VLMs) via natural language. This approach distinctively positions LLMs as implicit optimizers, circumventing traditional gradient-based methods, to enhance downstream vision tasks.

Methodological Framework

GLOV innovatively utilizes LLMs by meta-prompting them with a description of the downstream tasks, followed by generating and ranking suitable prompts for VLMs, like CLIP, particularly in zero-shot classification settings. These LLM outputs are iteratively refined based on their effectiveness, gauged through a defined fitness function that assesses performance on a few-shot training set.

A critical feature of GLOV is its novel steering mechanism, designed to implicitly direct the LLM’s text generation towards more effective prompts. This is achieved by embedding an offset vector, calculated by the difference between positive and negative solutions discovered in preceding steps, into the network's intermediate layers. This guides the LLM's output, optimizing its compatibility with the VLM’s preferences.

Empirical Evaluations and Numerical Insights

The GLOV framework was evaluated against 16 diverse datasets using dual-encoder (e.g., CLIP) and encoder-decoder (e.g., LLaVa) VLM architectures. The results indicate a significant enhancement in recognition performance, with improvements of up to 15.0% on dual-encoder models and an impressive 57.5% on encoder-decoder models. On average, improvements are recorded as 3.8% and 21.6% respectively.

This performance uplift underscores the potential of GLOV to discover prompts that are not only semantically relevant but also tailored for maximum efficacy in VLM applications. Importantly, GLOV achieves this without necessitating any parameter updates or fine-tuning, showcasing its adaptability and efficiency in optimizing vision tasks purely through language-guided methods.

Theoretical and Practical Implications

The GLOV approach challenges traditional optimization paradigms by demonstrating that LLMs, when guided appropriately, can serve as powerful tools for sparsely supervised tasks, enhancing the generalization capabilities of VLMs across diverse datasets. This suggests a broader applicability for LLMs in tasks requiring domain-specific language understanding, and highlights potential pathways for optimizing machine learning models that extend beyond visual domains.

Theoretically, this work pushes the boundaries of understanding how language processing capabilities can be intrinsically tied to visual learning systems. Practically, it implies a future where AI systems can be fine-tuned or adapted to new tasks through nuanced language interventions rather than extensive retraining processes.

Future Directions

The promising results and robust methodological framework of GLOV invite further exploration into its application across other modalities and AI models. Future research could explore the scalability of this method in more comprehensive multi-modal frameworks or across other challenging visual and non-visual domains.

In conclusion, the GLOV framework provides a compelling evidence for using LLMs as powerful optimizers for VLMs, demonstrating the potential of language-guided optimization techniques to significantly enhance AI model adaptability and performance in vision tasks. This novel approach warrants further investigation and could influence the development of more adaptive and efficient machine learning models in the era of foundation models.