I think this might be useful to ad creators as they dig in to all the new generative AI ad tools, and fine-tune their approach. I came across it looking for studies on image prompts, and image prompt structuring best practices a while ago. I wanted to know how to measure the effectiveness of an image prompt, and if it had been studied, what was the framework describing it. At the time, I didn't find many sources of prompt studies. Cosmic was one peripheral method to infer from.
"COSMic: A Coherence-Aware Generation Metric for Image Descriptions" (Source: https://arxiv.org/abs/2109.05281, PDF)
Here's the Gemini Summary:
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Gemini:
The document you provided is a scientific paper about a new metric for evaluating the quality of image captions, called COSMic. Here's a breakdown of the key points:
Introduction:
- Difficulty in automatically evaluating image captions
- Existing metrics focus on surface-level features or don't generalize well
- COSMic addresses this by considering the discourse goals of captions
Related Work:
- Different approaches to analyzing relationships between text and images
- Coherence-aware models in other NLP tasks
- Limitations of existing image caption evaluation metrics
COSMic's Approach:
- Leverages coherence relations (e.g., describes the image, tells a story)
- Trained on a new dataset with human-annotated coherence labels
- Uses pre-trained language models (BERT, ViLBERT) for better understanding
Methodology:
- RaCCoon dataset: human ratings on coherence-labeled captions
- COIN dataset: ground-truth captions for out-of-domain images
- COSMic Vanilla ( simpler, text-based) and COSMic ViLBERT (more complex, multimodal) versions
Evaluation:
- Compares COSMic with other metrics on out-of-domain image captions
- Measures agreement with human judgments using Kendall's Correlation Coefficient
Results:
- COSMic shows higher correlation with human ratings than other metrics
- This suggests it better captures the goals and information content of captions
Overall:
COSMic is a new metric for image caption evaluation that considers the discourse goals of captions. It achieves better performance than existing metrics by incorporating coherence information and using pre-trained language models. This approach has the potential to improve the quality of automatically generated image captions.
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Me: What about sentiment analysis?
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AI, specifically through a technique called sentiment analysis, can analyze text data and determine the emotional tone or opinion expressed. Here's a breakdown of how it works:
Natural Language Processing (NLP): This is a subfield of AI that allows computers to understand and process human language. Sentiment analysis tools leverage NLP techniques to break down text into its components (words,phrases) and analyze their meaning.
Machine Learning: Many AI-powered sentiment analysis tools use machine learning algorithms. These algorithms are trained on massive amounts of labeled text data, where each piece of text is categorized as positive, negative, or neutral (and sometimes even more nuanced emotions). By analyzing patterns in this data, the algorithms learn to identify emotional cues in new, unseen text.
Lexicon-based Approach: Some tools rely on sentiment lexicons, which are large databases of words with pre-assigned emotional scores. The software scans the text for words in the lexicon and assigns a sentiment score based on the words' emotional value.
AI-based sentiment analysis offers several advantages:
Scalability: AI can analyze large volumes of text data quickly and efficiently, which is impossible for manual analysis.
Accuracy: Machine learning algorithms can achieve high accuracy in sentiment detection, especially when trained on large and diverse datasets.
Nuance: Advanced sentiment analysis tools can go beyond basic positive/negative and identify more specific emotions like anger, joy, or sadness.
Here are some applications of sentiment analysis:
Customer service: Businesses can use sentiment analysis to understand customer feedback from reviews, social media posts, and surveys. This can help them identify areas for improvement and address customer concerns.
Market research: By analyzing social media conversations and online reviews, companies can gain insights into public opinion about their products, brands, and competitors.
Social media monitoring: Organizations can track sentiment around specific topics or events on social media,providing valuable insights for PR and crisis management.
Overall, AI-powered sentiment analysis is a powerful tool that can be used to understand people's opinions and emotions expressed in text data.
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Me: Knowing how sophisticated AI has become with sentiment analysis, even visually, I assume a prompt effectiveness measurement scale exists. Does it? I've only seen Cosmic.
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Gemini:
There isn't a universally accepted "prompt effectiveness measurement scale" yet, even though sentiment analysis is indeed advanced. Here's why:
- Prompt Effectiveness Depends on Context: A prompt's effectiveness depends on the specific task and desired outcome. A prompt for writing a funny poem will be measured differently than one for summarizing a research paper.
- Multiple Evaluation Metrics: There's no single metric to capture everything. Depending on the goal, you might consider accuracy, fluency, informativeness, or how well it aligns with a specific style or tone.
- Human Judgment Still Plays a Role: Especially for creative tasks, human evaluation is important. Sure, AI can tell if a poem is grammatically correct, but is it funny? That's a subjective call.
However, there are techniques for evaluating prompts:
- Task-Specific Metrics: Use established metrics for the desired task (e.g., accuracy for question answering).
- Human Evaluation: Have people rate the outputs generated by different prompts.
- A/B Testing: Compare the results of different prompts on a specific task.
The term "cosmic" might refer to early attempts at prompt evaluation frameworks, but it's not a widely used term.
The field of prompt engineering is still evolving, and standardized measurement is an ongoing area of research.
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I hope this summary is helpful!