Language is integral to our identity and belonging. It is closely entwined with our culture and community, our families and colleagues.
The same is true for industries. Each industry has a ‘language’ that is linked to it, and it sets them apart from other industries, for example, the in-house language for aviation is very different to that of farming.
General Large Language Models are insufficient for industry-specific chatbots
While general purpose
Large Language Models (LLMs) work well for conversational AI chatbots (like
ChatGPT), they are not up to the mark when it comes to specialised use cases. On the other hand, Custom Language Models (CLMs) that are focus-trained on industry-specific terms for an individual vertical can understand and accurately respond to industry-related queries.
Custom trained language models underpin high performing AI chatbots
How specialised Custom Language Models work is best explained with an example.
Imagine having a language model specifically designed to tackle the unique challenges of customer engagement in debt collection. This specialised model would have been trained to understand the intricacies of credit and collections, enabling it to provide valuable assistance and support in this field.
One huge plus of using such a language model is its deep understanding of the industry's terms. It has been trained on extensive datasets encompassing debt collection documents, industry regulations, customer interactions, and expert knowledge.
This means it can grasp the nuances of debt collection processes, legal requirements, and customer concerns, making it an invaluable asset in collections conversations.
With this custom language model as the foundation, collections chatbots perform their job at a much higher skill level. They can handle a variety of tasks, such as answering customer queries regarding debts, providing guidance on repayment options, and addressing common concerns with empathy and accuracy.
By speaking the language of debt collection, this model can ensure that customer interactions are personalised and tailored to the unique circumstances of each customer’s debt situation.
Moreover, the language model's training in credit and collections equips it to automate routine tasks, such as ID&V, account balance checks, generating customised payment reminders, and even analysing customer payment histories.
Say, for example, a customer reaches out with questions about their outstanding debt. The AI chatbot built on the debt collection language model can engage with the customer, understand their specific concerns, and provide clear and accurate explanations about the debt, repayment options, and potential consequences.
It can offer empathetic help and guide customers on their rights, responsibilities and available solutions. By addressing customer enquiries knowledgeably, this specially trained chatbot contributes to a positive customer experience while also supporting the debt collection process.
Let's take a closer look at how these custom language models benefit businesses.
Industry-specific language models bring a wealth of advantages
In-depth industry knowledge = better customer engagement: These models are trained on extensive datasets from a particular industry, allowing them to grasp the industry's terminology, jargon, and unique nuances, and capture the specific intricacies of the industry. This enables them to provide more relevant and helpful responses to queries.
Fast, accurate automation: Thanks to their understanding of industry-specific contexts, these models can offer quicker solutions built to industry needs. They can automate routine tasks, assist with customer support, and provide targeted recommendations. And while doing this, they relieve the workload of the live agents which gives them the space to deal with the more interesting and complex cases.
Compliance ensured: By training bots on niche language models, the regulations that pertain to a particular industry are built-in, which keeps the system within compliance boundaries.
Let's see how these language models work
Data collection: To train custom language models, vast amounts of data are collected from various industry sources such as technical documents, industry publications, customer interactions, and insights from experts. These datasets contain a wealth of information about the industry, including its terminology, concepts, and common scenarios.
Pre-training and fine-tuning: The language model goes through a two-step process. First, it undergoes pre-training, where it learns general language patterns and structures from a large body of text data. Then, it moves on to the fine-tuning phase, where it specialises in understanding and generating content relevant to the industry in question by training on the industry-related dataset.
Deployment and interaction: Once the model is trained, it is deployed as a conversational AI system. Users can interact with the model through digital interfaces, posing topical queries, seeking advice, or requesting information.
Conclusion
Custom Language Models are powerful customer service tools that give expertise and accuracy to contact centres. By understanding and speaking the language of a particular industry, these models facilitate the kind of customer interactions every business aspires to have.
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