Evaluating the quality of generated content, particularly in the context of natural language processing (NLP) and generative models, involves various techniques. These techniques can be broadly categorized into automatic metrics, human evaluation, and hybrid methods. Here are some commonly used techniques:

Automatic Metrics

1. BLEU (Bilingual Evaluation Understudy)

• Measures the similarity between the generated content and one or more reference texts using n-gram overlap.

2. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

• Focuses on recall and measures the overlap of n-grams between the generated content and reference texts.

3. METEOR (Metric for Evaluation of Translation with Explicit ORdering)

• Considers synonyms, stemming, and paraphrasing, making it more semantically aware than BLEU and ROUGE.

4. Perplexity

• Measures how well a probability model predicts a sample. Lower perplexity indicates better performance.

5. CIDEr (Consensus-based Image Description Evaluation)

• Designed for image captioning, but also applicable to text, focusing on consensus among multiple references.

6. BERTScore

• Uses BERT embeddings to evaluate the similarity of the generated text to reference text, capturing semantic similarities.

Human Evaluation

1. Fluency

• Assess how grammatically correct and natural the generated content is.

2. Relevance

• Measures how relevant the generated content is to the given input or prompt.

3. Coherence

• Evaluates how logically consistent and well-structured the content is.

4. Engagement

• Measures how engaging and interesting the content is to the reader.

5. Usefulness

• Assesses how useful the content is in fulfilling its intended purpose.

6. Adequacy

• Measures the extent to which the generated content conveys the same meaning as the reference content.

Hybrid Methods

1. Human-AI Collaboration

• Combines automatic metrics with human evaluation to balance efficiency and depth of assessment.

2. Error Analysis

• Involves detailed analysis of errors identified by both automatic and human evaluators to provide insights into model performance.

Advanced Techniques

1. Adversarial Testing

• Involves generating challenging test cases to evaluate robustness and identify weaknesses in the generated content.

2. Interactive Evaluation

• Uses interactive scenarios where humans interact with the generated content to assess its practical utility and performance in real-time applications.

3. User Studies

• Involves conducting surveys or studies with end-users to gather feedback on the quality and effectiveness of the generated content in real-world contexts.

Each technique has its strengths and limitations, and the choice of evaluation method often depends on the specific use case, the nature of the content, and the resources available. Combining multiple techniques can provide a more comprehensive assessment of content quality.

The post What are the different techniques for evaluating the quality of generated content? appeared first on Artificial Intelligence.

The jury is still out on what makes Gen Z’ers tick, but while the research is still ongoing there is much evidence to suggest that a marketing strategy utilizing machine learning is exponentially more effective with the next generation.

One thing is abundantly clear to every marketer worth his salt; Gen Z customers are “ninja-level” efficient at swatting away regular ads and pop-ups. They are strongly immune to hard sales and obvious sales content.

Despite all the difficulties that marketers are facing in reaching a wide Gen Z audience, Jonathan Jadali, CEO and Founder at Ascend Agency has found great success in leading Gen Z-focused startups to victory in this marketing struggle.

So what makes the typical Gen Z customer tick and how can businesses and startups build a brand that is appealing to them, utilizing cutting edge technologies?

Jadali shares the ways in which he has used a data and machine-learning strategy in getting many of his clients from obscurity to domination of the Gen Z market.

Train Your AI To Get A Bit Messy Sometimes

Content, as they say, is king, but the wrong kind of content isn’t even fit to be a pawn in this game. To get startups headed in the right direction, Jonathan often helps direct his clients at Ascend Agency on creating the right type of content for the right type of client. 

Emotional EQuity: How Leaders Use Empathy To Inspire Successful Teams

While most brands are focused on putting out well-curated video and image content in a bid to drive engagement on their social media platforms, Jadali advises that this might not be the best way to go if Gen Z’ers are your target audience. 

The ideal Gen Z customer thrives on spontaneous and ‘messy’ content. As Jadali states, “Gen Z customers are all about being real…they connect well with unfiltered and unedited content because it tends to feel less salesy than others.”

For instance, a makeup brand is better off posting a video of a makeup session, in front of a cluttered vanity table, than a photoshoot with a perfectly made-up face.

This is important to keep in mind when implementing any machine learning into your marketing strategy. Whether you are creating a chat bot, or building a data-driven marketing campaign – it’s important that your system learns to be imperfect. 

When AI or Machine Learning is used in marketing, sometimes it can come off as, well, robotic. Gen Z will be an important moment for machine learning marketing as it will help us get closer to contextual AI – machines that more accurately predict and reflect human behavior.

Gen Z wants to see the messiness of life and its process reflected in your content. Brands that do this,  are the brands that they are drawn to and often build loyalty for. 

Don’t Sell Them Products

How does it look? How effective is it? How satisfying is your service? All these are valid marketing questions and things that in the past had been asked by your millennial customer base.  

According to Jadali, these questions do not matter nearly as much to a Gen Z audience. 

“Clearly, customers want products that work and businesses that deliver, but with a Gen Z audience, that doesn’t seem to be the right way to lead in marketing to them.”

Having worked with both Fortune 500 companies and smaller startups alike in the last 3 years since Ascend Agency launched, Jadali is fairly certain that Gen Z customers are way more attracted to how your business makes them feel. 

This is where machine learning can really come in handy. Understanding your customers’ moods and habits can help you tap into what makes them feel great about themselves and the products  in their lives. 

Gen Z customers are tired of hearing about how amazing your product is, businesses have been hyping up their products for as long as businesses have existed and Gen Z’ers aren’t having any more of it. In Jonathan’s words, “Sell experiences, not products, and your products will head out of your door as well.”

“What dominant feeling do you want to evoke with your content?” A question that is popularly asked at the Ascend Agency office, is one that has helped brands build consistency in their content style and delivery and that has brought the Gen Z customers in their droves.

This question can be answered through aggregated customer data that helps you better understand the emotions from brands that they also engage with.   

Red Bull is a great example of a brand that utilizes data and machine learning in this manner. Their video content covers high-risk sports, like Skydiving, Bungee jumping, etc. From customer data processed by predictive analytics and machine learning systems, the dominant feeling Red Bull chose to evoke is one of courage and strength. 

The post Is Machine Learning The Key To Unlocking Gen Z Engagement? A Discussion With Jonathan Jadali Of Ascend appeared first on Artificial Intelligence.