Language Models as Black-Box Optimizers for Vision-Language Models

• URL: http://arxiv.org/abs/2309.05950v5
• Date: Tue, 14 May 2024 03:20:42 GMT
• Title: Language Models as Black-Box Optimizers for Vision-Language Models
• Authors: Shihong Liu, Zhiqiu Lin, Samuel Yu, Ryan Lee, Tiffany Ling, Deepak Pathak, Deva Ramanan,
• Abstract summary: Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. We aim to develop a black-box approach to optimize VLMs through natural language prompts.
• Score: 62.80817942316398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic hill-climbing procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit gradient direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we apply our framework to optimize the state-of-the-art black-box VLM (DALL-E 3) for text-to-image generation, prompt inversion, and personalization.

Related papers

• SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization [70.11167263638562]
  Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. We first present a simple yet well-crafted framework named name, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework.
  arXiv  Detail & Related papers  (2024-10-28T18:10:26Z)
• IPO: Interpretable Prompt Optimization for Vision-Language Models [40.83071220530289]
  This paper introduces a simple but interpretable prompt (IPO) IPO utilizes large language models (LLMs) to generate textual prompts dynamically. We incorporate a large multimodal model (LMM) to condition on visual content by generating image descriptions.
  arXiv  Detail & Related papers  (2024-10-20T14:10:22Z)
• GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models [44.82179903133343]
  Large Language Models (LLMs) act as implicits for Vision-Langugage Models (VLMs) Our GLOV meta-prompts an LLM with the downstream task description, querying it for suitable VLM prompts. We evaluate our GLOV on 16 diverse datasets using two families of VLMs.
  arXiv  Detail & Related papers  (2024-10-08T15:55:40Z)
• LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Models for Referring Expression Comprehension [45.856469849910496]
  We propose a method for 'black-box' adaptation of Vision Language Models (VLMs) for the Referring Expression (REC) task using Large Language Models (LLMs) LLMs capitalizes on the reasoning abilities of LLMs, improved with a light fine-tuning, to select the most relevant bounding box matching the referring expression. Our approach offers several advantages: it enables the adaptation of closed-source models without needing access to their internal workings.
  arXiv  Detail & Related papers  (2024-09-18T12:32:25Z)
• Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge [76.45868419402265]
  multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets. However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs. This paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models, into MLLMs.
  arXiv  Detail & Related papers  (2024-07-05T17:43:30Z)
• Hidden in Plain Sight: Exploring Chat History Tampering in Interactive Language Models [12.920884182101142]
  Large Language Models (LLMs) have become prevalent in real-world applications, exhibiting impressive text generation performance. To behave interactively, LLM-based chat systems must integrate prior chat history as context into their inputs, following a pre-defined structure. This paper introduces a systematic methodology to inject user-supplied history into LLM conversations without any prior knowledge of the target model.
  arXiv  Detail & Related papers  (2024-05-30T16:36:47Z)
• Learning to Prompt with Text Only Supervision for Vision-Language Models [107.282881515667]
  One branch of methods adapts CLIP by learning prompts using visual information. An alternative approach resorts to training-free methods by generating class descriptions from large language models. We propose to combine the strengths of both streams by learning prompts using only text data.
  arXiv  Detail & Related papers  (2024-01-04T18:59:49Z)
• CLAMP: Contrastive LAnguage Model Prompt-tuning [89.96914454453791]
  We show that large language models can achieve good image classification performance when adapted this way. Our approach beats state-of-the-art mLLMs by 13% and slightly outperforms contrastive learning with a custom text model.
  arXiv  Detail & Related papers  (2023-12-04T05:13:59Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.