Prompt Engineering News
Attention Prompting on Image
Attention Prompting on Image is a technique that overlays a text-query-guided attention heatmap on the image, guiding the model to focus on important areas based on the task or question at hand. 🎯
How it works:
- A VLM generates a heatmap highlighting relevant image regions
- This heatmap is overlaid on the image, creating a modified version emphasizing key areas
- The model uses this updated image to generate more accurate answers
Why it matters: By focusing the model's attention on important parts of the image, this technique significantly improves accuracy on vision-language tasks without needing complex training or fine-tuning.
Context Optimization (CoOp)
Context Optimization (CoOp) automates prompt creation by using learnable vectors instead of static words. This allows VLMs to adapt more quickly to new tasks with minimal labeled data, improving performance across image classification tasks.
What is CoOp? It creates a shared prompt for all classes, tailoring prompts to individual image classes for fine-grained tasks like distinguishing dog breeds.
Why CoOp is better:
- Faster and easier than manual prompt engineering
- Adapts dynamically with small training data, unlike zero-shot models
Prompting Tweets
Date | Author | Tweet |
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2024-10-22 |
We knew this would be the case last year when we ran HackAPrompt. Here's a write up on the history of Prompt Injections if interested. See also our new course on AI Red Teaming. |
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2024-10-18 |
Interested in how Attention Prompting on Image can enhance vision-language tasks? Learn more here. |
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2024-10-16 |
Tuning VLMs through hand-crafted prompts or fine-tuning presents drawbacks like inefficiency and difficulty adapting to new tasks. Context Optimization (CoOp) addresses these by automating prompt creation and enhancing flexibility. |
LLM Adoption Forecast
LLM Milestones
Large Language Models (LLMs) have rapidly evolved, achieving remarkable milestones in natural language processing. This timeseries highlights some of the most significant breakthroughs that have propelled LLM capabilities forward. 🚀
While early LLMs laid the groundwork, recent advancements like GPT-3 and multimodal models have unlocked new frontiers in language understanding and generation. As research continues, we can expect LLMs to tackle increasingly complex tasks and push the boundaries of what's possible with AI.