Large Language Models (LLMs) are AI systems trained on extensive datasets to understand and generate human language. They handle tasks like intent understanding, content creation, and translation, making them useful in enterprise settings. LLMs enable natural, efficient conversations, reduce training time, and lower costs but face challenges with accuracy, privacy, and ethical biases.
What do LLMs do?
A large language model (LLM) is a type of artificial intelligence (AI) that has been trained on a massive dataset of text and code.
This allows it to perform a variety of tasks, including:
- Natural Language Understanding - Understand complex intents
- Natural Language Generation - Generate completely new content
- Translation
Some well known LLMs are:
- GPT-4 - Developed by OpenAI
- LaMDA: Developed by Google AI
LLMs can also be trained on enterprise data, allowing them to answer more specific content questions.
LLM benefits in Enterprise Conversational AI
- More Human Conversations: LLMs can understand and respond to far more complex conversations improving containment and increasing customer satisfaction.
- Reduced Training: LLMs are already trained on Natural Language Processing (NLP) and simply need to self learn to be able to answer enterprise specific information.
- Faster Time to Value: With no training and no programming of responses LLM based bots can be deployed much more quickly than traditional NLP based bots.
- Cost Effectiveness: LLM based bots can be far more cost effective to create and operate. Increased containment means a bot can handle great volumes of inquiries without any help from a human agent, thus reducing headcount budget.
LLM risks in Enterprise Conversational AI
- Accuracy: LLM Bots Hallucinate. There is nothing inherent in LLMs that make them accurate. LLMs are non- deterministic. They can answer the same question in two completely different ways. Check out the Botium Fact Checker.
- Misuse: LLM Bots can answer some questions that an enterprise would not want answered. For example: what's the best way to defraud a specific enterprise? Check out Botium Misuse Checker.
- Privacy, Security and Regulation: LLM Bots may inadvertently leak regulatory controlled information such as privacy data. LLM Bots may also be impacted by deep fakes or plagiarism.
- Biases: LLM Bots reflect the biases that are present in the underlying training data. There is no inherent ‘ethical standard’ within the LLM.
Why hasn’t self-service eliminated human agents already?
- Complexity: Complex and difficult interactions require a human agent to understand what the customer wants and turn this into actions and internal processes.
- Broken Processes: The process and the self-service automation is broken and doesn't deliver the expected result.
- Ease of Use: People will always take the most convenient path with the least amount of perceived effort.
- Demographics: Portions of the population aren’t adopting new technologies.