Keeping up with rising customer expectations is an uphill battle for businesses. Customers lack loyalty, and one or two bad experiences make them look to your competitor. How do you keep winning in situations like this? Technology! Specifically, bringing AI-powered experiences to the enterprise that deliver exceptional customer experiences.
Artificial intelligence (AI) intelligent virtual assistants (IVA) automate and optimize the customer experience efficiently. However, a successful IVA usually requires extensive training, which can be time-consuming and expensive.
In its latest release, the Kore.ai XO platform addresses these obstacles with the power of large language models (LLMs) and generative AI technologies. Zero-shot and Few-shot learning models.
These models perform tasks on new, unseen data, limiting no learning to that particular task. This is possible because these models are pre-trained on billions of data points and can make accurate predictions on new and unseen data. They allow for more effective and efficient use of available data while reducing the amount of manual effort required for labeling and training models. Let’s explore them further.
The Zero-Shot Model is a powerful tool that can help businesses build and deploy IVAs without any training and achieve rapid development. This is useful in situations where there are many possible outcomes and it would be impractical or impossible to collect labeled data for all of them.
In the Kore.ai XO platform, the model is integrated with Open AI GPT models, which allows for efficient processing of customer requests. Without prior training, the model can identify intent and extract points from customer queries, facilitating accurate responses from virtual assistants. By eliminating the need for training, businesses can reduce costs and accelerate development. Customers can enjoy a superior conversational experience without waiting for virtual assistant training.
Let’s explore how to use the model.
- The model allows users to define the intent to be identified. Intent names should be more descriptive for the model to work effectively, for example “Credit card transaction dispute.“
- The user must enable the “Zero-Shot” grid type during the training process.
- The system discovers the most appropriate intent by matching the user statement to the defined intent names without the need for training statements. It then responds with appropriate intentions.
- Intents detected by the Zero-Shot model are considered final matches.
Here are some considerations when using the Zero-Shot model:
- To integrate the Kore.ai XO platform with OpenAI, you must provide an API key. Intent names and user expressions will be shared with OpenAI.
- It is important to note that the Zero-Shot network type applies only to the machine learning (ML) engine, not to the base meaning (FM), knowledge graph (KG) or feature engines. The platform continues to use patterns for intent matching with the FM engine.
- When Zero-Shot is enabled, all ML engine matches are considered final.
- The default specific score is set to 80% based on natural language processing (NLP) performance and accuracy.
The zero-shot model uses integration with OpenAI to identify targets and generate responses. That means that information is shared with OpenAI for processing and feedback, which can be a concern in some cases, and you need to purchase an OpenAI license separately.
To overcome these challenges, Kore.ai also introduced the Few-Shot Model, a more powerful model that uses the open-source LLM hosted on the Kore.ai server.
The Few-Shot model also leverages the power of large language models and eliminates or minimizes the training effort. Unlike the Zero-Shot Model, the Few-Shot Model allows instruction to be delivered using task names; it is much faster than traditional models. It requires only 1/10th of the training required to operate it. That means a 10x increase in development speed.
The model uses Kore.ai’s specially tuned LLM, pre-trained with large data sets to handle customer queries, which provides greater consistency in responding to customer queries. In addition, the model is robust and secure, as it does not share data with third-party sources and requires no additional activation costs.
Here are some considerations when using the Few-shot model:
- Turn off Multi-objective models and configure the minimum ML threshold to 0.6, and the final ML score is set to 90% (0.9).
- Disable Intent Recovery
- Added a default value of 5 to the “ML Suggestion Proximity” (for ML Engine) list.
- Exact Task Name Match’ (for FM Engine) is added to the list by default and set to Disabled.
Which one is best for your IVA development?
Both the Zero-Shot and Few-Shot models are designed to help businesses accelerate their conversational AI journey and deliver superior customer experiences. However, the best choice will depend on the specific needs and goals of each individual business.
When considering the Zero-Shot model, businesses should keep in mind that it is best suited for simple tasks such as answering frequently asked questions or providing basic information. The model may not be as effective for more complex problems that require a deeper understanding of the customer’s needs.
On the other hand, the Few-Shot model is better suited for complex tasks and solves aliases and false positives because it requires some training to operate effectively. Businesses can provide additional training as needed, making it a more flexible option. In addition, the model’s ability to deliver consistently high performance can help businesses provide a more personalized experience for their customers.
Both models offer advantages and disadvantages, and businesses should carefully consider their needs before making a decision. However, it is clear that these models can help businesses achieve their goals more effectively and efficiently.
In conclusion, intelligent virtual assistants are a valuable tool for businesses looking to provide superior customer service. Zero-Shot and Few-Shot models offer new ways to achieve this goal without requiring extensive training or high costs.
The Kore.ai XO Platform has revolutionized IVA development by introducing these flexible and efficient models; they can help businesses positively engage with their customers anywhere, anytime. By carefully considering their needs and goals, businesses can choose the model that best suits them and succeed in this competitive market.