Efficiently Answer Business Questions with Cohere's Multilingual Model

Revolutionizing Customer Support with AI: A Guide to Coheres’ Multilingual Model

In today's globalized market, businesses often encounter diverse customer queries in various languages. This can lead to duplicated questions and overwhelming workloads for customer support teams. Luckily, Cohere has introduced an innovative solution with its new multilingual model, enabling companies to efficiently answer customer inquiries.

The Power of Cohere’s Multilingual Model

This tutorial explores how to utilize embeddings to streamline customer interaction, particularly for businesses like hotels experiencing a surge of multilingual inquiries.

Real-world Application: Answering Hotel Queries

Imagine owning a hotel with guests from around the world. You’re faced with numerous questions, many of which arrive in different languages: English, German, French, and Chinese. Cohere’s new model allows us to cluster these questions for more efficient responses.

Key Features of Cohere’s Model

Multilingual Semantic Search: Improve search results seamlessly, regardless of the language used.
Aggregate Customer Feedback: Simplify the organization of multilingual feedback for more informed decision-making.
Cross-Lingual Zero-Shot Content Moderation: Detect harmful content in 100+ languages, enhancing online community safety.

How Cohere’s Model Works

Cohere’s multilingual text understanding model utilizes semantic vector space mapping. This technique positions related texts closer together, yielding better outcomes than traditional keyword searches. Unlike previous models using machine translation, Cohere's model is trained on a dataset of nearly 1.4 billion question/answer pairs, capturing essential nuances in over 100 languages.

Let's Dive Into Our Example

For our hotel, we want to analyze the following multilingual queries:

Question 1: What are the food options available?
Question 2: Is there a pool at the hotel?
Question 3: Where can I find a charging station?
Question 4: What movies are playing?
Question 5: Is breakfast included?

Upon reviewing these queries, we can identify five distinct topic clusters: Food, Pool, Charging Station, Theater, and Breakfast.

Automating the Clustering Process

To automate clustering using Cohere’s model, we recommend accessing the Cohere Playground. Here's how you can get started:

Input your questions in the 'Texts' field.
On the right side, set your parameters: choose the model 'multilingual-22-12' and set truncation to 'None.'
Click on 'Calculate' to see the model's clustering results.

After processing, you’ll find that the model has successfully categorized your questions into 5 clusters, highlighting the similarities between them.

Moving to Custom Code with Python

Once you grasp the functionality within the Playground, you can export your example code. Simply click the 'Export code' button and select Python as your programming language. This allows you to customize the embeddings further for your unique use case.

Join the AI Revolution

Interested in enhancing your skills and knowledge in AI technologies? Join our AI Hackathons and collaborate with mentors to develop AI-based tools that can transform the world! Find upcoming events at lablab.ai.

Conclusion

With Cohere's multilingual model, businesses can not only streamline their customer support process but also improve the quality and efficiency of their responses. The ability to cluster questions based on meaning rather than language barriers sets a new standard for multilingual customer engagement.